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Optimization of kidney dysfunction prediction in diabetic kidney disease using targeted metabolomics

  • Isabel Ibarra-González
  • Ivette Cruz-Bautista
  • Omar Yaxmehen Bello-Chavolla
  • Marcela Vela-Amieva
  • Rigoberto Pallares-Méndez
  • Diana Ruiz de Santiago Y Nevarez
  • María Fernanda Salas-Tapia
  • Ximena Rosas-Flota
  • Mayela González-Acevedo
  • Adriana Palacios-Peñaloza
  • Mario Morales-Esponda
  • Carlos Alberto Aguilar-Salinas
  • Laura del Bosque-Plata
Original Article
Part of the following topical collections:
  1. Diabetic Nephropathy

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.

Keywords

Metabolomics Type 2 diabetes Diabetic kidney disease Filter paper Amino acids Acylcarnitines 

Abbreviations

DKD

Diabetic kidney disease

DBS

Dried blood samples

T2D

Type 2 diabetes

eGFR

Estimated glomerular filtration rate

A1C

Glycosylated hemoglobin

ACEI/ARB

Angiotensin-converting enzyme inhibitors/angiotensin II receptor blockers

SBP

Systolic blood pressure

DBP

Diastolic blood pressure

U/B ratio

Ratio or urinary divided by blood concentration of measured metabolites

ARG

Arginine

CIT

Citrulline

GLY

Glycine

ALA

Alanine

LEU

Leucine + isoleucine

MET

Methionine

PHE

Phenylalanine

TYR

Tyrosine

VAL

Valine

ORN

Ornithine

PRO

Proline

SA

Succinylacetone

C0

Free carnitine

C2

Acetylcarnitine

C3

Propionylcarnitine

C4OH\C3DC

3-Hydroxybutyryl + malonyl carnitine

C5OH\C4DC

3-Hydroxyisovaleril + methylmalonyl carnitine

C5DC\C6OH

Glutaryl + 3-hydroxyhexanoyl carnitine

C6DC

Adipylcarnitine

C4

Butyrylcarnitine

C5

Isovalerylcarnitine

C5:1

Tiglylcarnitine

C6

Hexanoylcarnitine

C8

Octanoylcarnitine

C8:1

Octenoylcarnitine

C16

Decanoylcarnitine

C16:1

Decenoylcarnitine

C16:1OH

Decadienoylcarnitine

C16OH

Dodecanoylcarnitine

C10

Dedecenoylcarnitine

C10:1

Tetradecanoylcarnitine

C10:2

Tetradecenoylcarnitine

C12

Tetradecadyenylcarnitine

C12:1

3-Hydroxy-tetradecanoylcarnitine

C14

Hexadecenoylcarnitine

C14:1

Hexadecenoylcarnitine

C14:2

3-Hydroxy-hexadecanoylcarnitine

C14OH

3-Hydroxy-hexadecenoylcarnitinae

C18

Octadecanoylcarnitine

C18:1

Octadecenoylcarnitine

C18:1OH

Octadecenoylcarnitine

C18:2

3-Hydroxy-octadecanoylcarnitine

C18OH

3-Hydroxy-octadecanoylcarnitine

Notes

Acknowledgements

All authors approved the submitted version. All the authors would like to thank the staff of the Endocrinology and Metabolism Department for all their support, particularly to Lucia Guillen-Pineda, Maria Del Carmen Moreno-Villatoro, María Guadalupe López-Carrasco and Maria Del Carmen Cruz-Lopez Adriana. We are thankful to the study volunteers for all their work and support throughout the realization of the study. OYBC would like to thank PECEM and Conacyt for their support in his research.

Author contributions

Research idea and study design: IIG, ICB, OYBC, CAAS, LDBP, MVA; data acquisition: RPM, DRSN, MFST, XRF, MGA, APP, MME, OYBC; data analysis/interpretation: OYBC, ICB, IIG; statistical analysis: OYBC, IIG, MVA; manuscript drafting: IIG, ICB, OYBC, MVA, CAAS, LDBP; supervision or mentorship: CAAS, LDBP, MVA. Each author contributed important intellectual content during manuscript drafting or revision and accepts accountability for the overall work by ensuring that questions pertaining to the accuracy or integrity of any portion of the work are appropriately investigated and resolved.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation (institutional and national) and with the Helsinki Declaration of 1975, as revised in 2008.

Informed consent

Written informed consent was obtained before the examination from each patient, as well as the approval from our institutional ethics committee.

Supplementary material

592_2018_1213_MOESM1_ESM.doc (69 kb)
Supplementary material 1 (DOC 69 KB)

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Copyright information

© Springer-Verlag Italia S.r.l., part of Springer Nature 2018

Authors and Affiliations

  • Isabel Ibarra-González
    • 1
    • 2
  • Ivette Cruz-Bautista
    • 3
    • 4
    • 5
  • Omar Yaxmehen Bello-Chavolla
    • 3
    • 6
  • Marcela Vela-Amieva
    • 2
  • Rigoberto Pallares-Méndez
    • 3
  • Diana Ruiz de Santiago Y Nevarez
    • 3
  • María Fernanda Salas-Tapia
    • 3
  • Ximena Rosas-Flota
    • 3
  • Mayela González-Acevedo
    • 3
  • Adriana Palacios-Peñaloza
    • 3
  • Mario Morales-Esponda
    • 3
  • Carlos Alberto Aguilar-Salinas
    • 3
    • 4
    • 5
  • Laura del Bosque-Plata
    • 7
  1. 1.Unidad de Genética de la Nutrición, Instituto de Investigaciones BiomédicasUNAM-Instituto Nacional de PediatríaMexico CityMexico
  2. 2.Laboratorio de Errores Innatos del Metabolismo y TamizInstituto Nacional de PediatríaMexico CityMexico
  3. 3.Unidad de Investigación en Enfermedades MetabólicasInstituto Nacional de Ciencias Médicas y Nutrición Salvador ZubiránMexico CityMexico
  4. 4.Departamento de Endocrinología y MetabolismoInstituto Nacional de Ciencias Médicas y Nutrición Salvador ZubiránMexico CityMexico
  5. 5.Tecnológico de MonterreyEscuela de Medicina y Ciencias de la SaludMonterreyMexico
  6. 6.MD/PhD (PECEM) Program, Facultad de MedicinaUniversidad Nacional Autónoma de MéxicoMexico CityMexico
  7. 7.Laboratorio de Nutrigenética y NutrigenómicaInstituto Nacional de Medicina GenómicaMexico CityMexico

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