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

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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.

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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

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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.

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Authors

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.

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Correspondence to Laura del Bosque-Plata.

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The authors declare that they have no conflict of interest.

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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.

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

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Ibarra-González, I., Cruz-Bautista, I., Bello-Chavolla, O.Y. et al. Optimization of kidney dysfunction prediction in diabetic kidney disease using targeted metabolomics. Acta Diabetol 55, 1151–1161 (2018). https://doi.org/10.1007/s00592-018-1213-0

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