Review
Copyright ©The Author(s) 2016.
World J Diabetes. Jul 25, 2016; 7(14): 290-301
Published online Jul 25, 2016. doi: 10.4239/wjd.v7.i14.290
Table 1 Advantages of novel biomarkers in the early diagnosis of diabetic kidney disease
BiomarkerValidation study designSample sizeType of diabetesSpecimenAdvantagesRef.
CysCCO52[38]2SerumNot affected by lean body mass[35-39]
30[39]Estimates more accurate than creatinine-based ones when GFR > 60 mL/min per 1.73 m2
NGALCC1122UrineIndicator of glomerular hyperfiltration[44]
KIM1CC1122UrineIndicator of glomerular hyperfiltration[44]
NAGCC4341UrineBaseline level predicts development of DKD[51]
CC9462[52]
8-oxodGPC3962UrineBaseline level predicts development of DKD[59]
PentosidineCC4341UrineBaseline level predicts progression of albuminuria[51]
TNFR1/2RC6281SerumBaseline level predicts development of advanced CKD[65]
RC4102[66]
Table 2 In vitro and in vivo renal cell models demonstrating the potential involvement of miRNAs in development of diabetic kidney disease
miRNASpeciesSpecimenmiRNA expressionMechanism of actionRef.
miR-192Mice/RatM, Te, KTInconsistent resultsInteraction with TGFβ-associated and other pro-fibrotic genes[94-96]
HumanTe, KTReduced[97]
miR-216aMiceM, KTElevated[98]
miR-377MiceM, KTElevated[99]
HumanM
miR-29cMiceP, KTElevated[100]
miR-200b/cMiceM, KTElevated[101]
miR-21MiceKTElevated[102]
HumanTe
miR-1207-5pHumanP, M, TeElevated[103]
miR-200aRatTeReduced[104]
MiceKT
miR-23bMiceKTReduced[105]
HumanTe, HEK-293A
miR-93MiceP, En, KTReducedRegulation of VEGF expression[106]
miR-25RatM, KTReducedRegulation of NOX4 expression[107]
miR-451MiceM, KTReducedTargeting YwhaZ and p38 MAPK signaling pathways[108]
Table 3 Urinary and serum miRNA profiles in patients with diabetic kidney disease
Type of diabetesSpecimenmiRNA expressionRef.
1UrineDecreasedIncreased[109]
miR-323b-5p, miR-221-3p, miR-524-5p, miR-188-3pmiR-214-3p, miR-92b-5p, hsa-miR-765, hsa-miR-429, miR-373-5p, miR-1913, miR-638
1UrineDecreasedIncreased[110]
miR-155, miR-424miR-130a, miR-145
2UrinemiR-29 expression positively correlated to the severity of albuminuria[111]
2BloodReduced expression of miR-let-7a[112]
Table 4 Applications of metabolomics in the diagnosis of diabetic kidney disease
SpecimenPanelApplicationRef.
PlasmaFatty acids C10:0, C12:0, C14:0, C16:1n-9, C16:0, C18:2, C18:1n-9, C18:1n-11, C18:0, C20:4, C20:5, C20:3, C20:2, C20:0, C22:6Diverse profiles in different stages of DKD[122]
PlasmaPhospholipids C18:2-LPC, C16:0/18:1-PE, pC18:0/20:4-PE, C18:0/22:6-PI, C18:0/18:0-PS, dC18:0/20:2-SMDiagnosis of DKD[123]
Serumγ-butyrobetaine, SDMA, azelaic acid, MID 114, MID 127Diagnosis of DKD[124]
Urine3-hydroxy isovalerate, aconitic acid, citric acid, 2-ethyl 3-OH propionate, glycolic acid, homovanillic acid, 3-hydroxy isobutyrate, 2-methyl acetoacetate, 3-methyl adipic acid, 3-methyl crotonyl glycine, 3-hydroxy propionate, tiglylglycine, uracilReduced expression in DKD patients[125]
Plasma and urinePlasma: Histidine, butenoylcarnitine Urine: Hexose, glutamine, tyrosineAddition to the original predictive model improved risk estimation for albuminuria progression[126]
PlasmaP-cresol sulfate, phenylacetylglutamine, myoinositol, pseudouridine, uratePredicting progression toward ESRD[127]