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Optimization of kidney dysfunction prediction in diabetic kidney disease using targeted metabolomics.
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- Author(s): Ibarra-González, Isabel; Cruz-Bautista, Ivette; Bello-Chavolla, Omar Yaxmehen; Vela-Amieva, Marcela; Pallares-Méndez, Rigoberto; Ruiz de Santiago Y Nevarez, Diana; Salas-Tapia, María Fernanda; Rosas-Flota, Ximena; González-Acevedo, Mayela; Palacios-Peñaloza, Adriana; Morales-Esponda, Mario; Aguilar-Salinas, Carlos Alberto; del Bosque-Plata, Laura
- Source:
Acta Diabetologica; Nov2018, Vol. 55 Issue 11, p1151-1161, 11p- Subject Terms:
- Source:
- Additional Information
- Abstract: Aims: Metabolomics have been used to evaluate the role of small molecules in human disease. However, the cost and complexity of the methodology and interpretation of findings have limited the transference of knowledge to clinical practice. Here, we apply a targeted metabolomics approach using samples blotted in filter paper to develop clinical-metabolomics models to detect kidney dysfunction in diabetic kidney disease (DKD).Methods: We included healthy controls and subjects with type 2 diabetes (T2D) with and without DKD and investigated the association between metabolite concentrations in blood and urine with eGFR and albuminuria. We also evaluated performance of clinical, biochemical and metabolomic models to improve kidney dysfunction prediction in DKD.Results: Using clinical-metabolomics models, we identified associations of decreased eGFR with body mass index (BMI), uric acid and C10:2 levels; albuminuria was associated to years of T2D duration, A1C, uric acid, creatinine, protein intake and serum C0, C10:2 and urinary C12:1 levels. DKD was associated with age, A1C, uric acid, BMI, serum C0, C10:2, C8:1 and urinary C12:1. Inclusion of metabolomics increased the predictive and informative capacity of models composed of clinical variables by decreasing Akaike’s information criterion, and was replicated both in training and validation datasets.Conclusions: Targeted metabolomics using blotted samples in filter paper is a simple, low-cost approach to identify outcomes associated with DKD; the inclusion of metabolomics improves predictive capacity of clinical models to identify kidney dysfunction and DKD-related outcomes. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of Acta Diabetologica is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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