Publication
Metabolic dysfunction biomarkers as predictors of early diabetes
| dc.contributor.author | Luís, Carla | |
| dc.contributor.author | Baylina, Pilar | |
| dc.contributor.author | Soares, Raquel | |
| dc.contributor.author | Fernandes, Rúben | |
| dc.date.accessioned | 2024-11-25T11:11:26Z | |
| dc.date.available | 2024-11-25T11:11:26Z | |
| dc.date.issued | 2021-10-27 | |
| dc.description.abstract | During the pathophysiological course of type 2 diabetes (T2D), several metabolic imbalances occur. There is increasing evidence that metabolic dysfunction far precedes clinical manifestations. Thus, knowing and understanding metabolic imbalances is crucial to unraveling new strategies and molecules (biomarkers) for the early-stage prediction of the disease’s non-clinical phase. Lifestyle interventions must be made with considerable involvement of clinicians, and it should be considered that not all patients will respond in the same manner. Individuals with a high risk of diabetic progression will present compensatory metabolic mechanisms, translated into metabolic biomarkers that will therefore show potential predictive value to differentiate between progressors/non-progressors in T2D. Specific novel biomarkers are being proposed to entrap prediabetes and target progressors to achieve better outcomes. This study provides a review of the latest relevant biomarkers in prediabetes. A search for articles published between 2011 and 2021 was conducted; duplicates were removed, and inclusion criteria were applied. From the 29 studies considered, a survey of the most cited (relevant) biomarkers was conducted and further discussed in the two main identified fields: metabolomics, and miRNA studies. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.citation | Luís, C., Baylina, P., Soares, R., & Fernandes, R. (2021). Metabolic dysfunction biomarkers as predictors of early diabetes. Biomolecules, 11(11), Artigo 11. https://doi.org/10.3390/biom11111589 | pt_PT |
| dc.identifier.doi | 10.3390/biom11111589 | pt_PT |
| dc.identifier.eissn | 2218-273X | |
| dc.identifier.uri | http://hdl.handle.net/10400.22/26457 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | MDPI | pt_PT |
| dc.relation | This research was funded by the FCT—Fundação para a Ciência e Tecnologia (REF UID/BIM/04293/2019), and by the following scholarships: (Ref. SAICT2016/FEDER/BIO4DIA/BTI) and (SFRH/BD/146489/2019). | pt_PT |
| dc.relation.publisherversion | https://www.mdpi.com/2218-273X/11/11/1589 | pt_PT |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | pt_PT |
| dc.subject | Prediabetes | pt_PT |
| dc.subject | Diabetes | pt_PT |
| dc.subject | Biomarkers | pt_PT |
| dc.subject | Early diagnosis | pt_PT |
| dc.title | Metabolic dysfunction biomarkers as predictors of early diabetes | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 15 | pt_PT |
| oaire.citation.startPage | 1 | pt_PT |
| oaire.citation.title | Biomolecules | pt_PT |
| oaire.citation.volume | 11(11) | pt_PT |
| person.familyName | BAYLINA MACHADO | |
| person.familyName | Fernandes | |
| person.givenName | PILAR | |
| person.givenName | Rúben | |
| person.identifier | 635792 | |
| person.identifier.ciencia-id | 1419-F23D-4920 | |
| person.identifier.ciencia-id | 0D1F-4090-E82A | |
| person.identifier.orcid | 0000-0002-3740-862X | |
| person.identifier.orcid | 0000-0001-8933-3984 | |
| person.identifier.rid | B-5134-2010 | |
| person.identifier.scopus-author-id | 56534079700 | |
| person.identifier.scopus-author-id | 57640135700 | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | article | pt_PT |
| relation.isAuthorOfPublication | b1482a24-d9d8-419d-af68-6df1a75afb3f | |
| relation.isAuthorOfPublication | 9e6c397c-62d8-4a40-8ec7-ad05ae0ebcc4 | |
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