BIOMARKERS THAT PREDICT PROGRESSION OF CHRONIC KIDNEY DISEASE MORE ACCURATELY THAN ESTABLISHED CLINICAL PARAMETERS

E OWENS1,2,3, W  HOY1,2,4, KS TAN1,5, E LENNAN5, A CAMERON1,4, T HUMPHRIES2,3, R ELLIS2,3, H HEALY1,2,6, G GOBE1,2,3

1NHMRC CKD CRE (CKD.QLD), The University of Queensland, Brisbane, Australia , 2Faculty of Medicine, The University of Queensland, Brisbane , Australia, 3Kidney Disease Research Collaborative, Princess Alexandra Hospital, The University of Queensland, Brisbane, Australia, 4Centre for Chronic Disease, Faculty of Medicine, The University of Queensland, Brisbane, Australia, 5Renal Medicine, Metro South Hospital and Health Service, Logan Hospital, Logan, Australia, 6Kidney Health Service, Royal Brisbane and Women’s Hospital , Brisbane, Australia

Aim: To identify biomarkers that predict progression of chronic kidney disease (CKD).
Background: Current biomarkers (serum creatinine/sCr, albuminuria, estimated glomerular filtration rate/eGFR and albumin-creatinine ratio/ACR) are insufficiently accurate to predict CKD progression. A panel of specific biomarkers was evaluated for predictive accuracy.
Methods: CKD patients, 42 progressive and 30 non-progressive, enrolled in the CKD Biobank between November/2017 and October/2018, were followed until December/2019. Progression was defined by a ≥30% decline in eGFR, initiation of dialysis or kidney transplantation. Kidney measurements were taken at enrolment, along with plasma and serum biomarker measurements, and periodically during follow-up. Baseline biomarker concentrations were compared between groups by independent t-tests or Mann-Whitney U-tests. Predictive models were calculated by linear discriminant analysis, and their accuracy compared.
Results: CKD patients with progression had higher baseline levels of neutrophil gelatinase-associated lipocalin/NGAL [Z=2.21, p<0.05], osteopontin [Z=2.76, p<0.01], tissue factor/TF [Z=3.37, p<0.001], tumour necrosis factor/TNF-α [Z=2.26, p < 0.05], TNFR-1 [Z=3.45, p<0.001], TNFR-2 [t(65)=2.43, p<0.05], stem cell factor/SCF [t(65)=4.66, p<0.0001], tryptase [Z=2.65, p<0.01], calculated osmolality [t(62)=2.58, p<0.01] and phosphate [Z=1.98, p<0.05], in addition to sCr [Z=4.40, p<0.0001], cystatin-c [t(65)=2.74, p<0.01] and urea [Z=3.79, p<0.0001]. Baseline eGFR [Z=3.92, p<0.0001) and levels of bicarbonate [Z=3.09, p<0.01], haematocrit [t(63)=2.53, p<0.01] and haemoglobin [t(64)=2.76, p<0.01] were also lower. A panel of eGFR, sCr, cystatin-c, urea, TNF-α, sTNFR-1, sTNFR-2, SCF, tryptase, NGAL, TF, bicarbonate, calculated osmolality and haematocrit had a predictive accuracy for progression of 96%, while sCr, albuminuria, eGFR and ACR had accuracies of 71%, 58%, 68% and 58%, respectively.
Conclusions: 17 biomarkers at baseline differentiated subsequently progressive and non-progressive CKD patients. A panel of 14 of these biomarkers more accurately predicted progression than sCr, eGFR, albuminuria or ACR.


Biography:
Evan Paul Owens is a PhD candidate with The University of Queensland conducting clinical chronic kidney disease research with the Kidney Disease Research Collaborative and the NHMRC Chronic Kidney Disease Centre of Research Excellence. Evan’s research is focused in two areas. Firstly, developing a chronic kidney disease Biobank into a bioresource for future use. Secondly, investigating progressive chronic kidney disease in terms of how it is defined, biomarkers of progression, and development of a predictive model that can more accurately predict progressive chronic kidney disease than current clinical options.
Evan Paul Owens is a PhD candidate with The University of Queensland conducting clinical chronic kidney disease research with the Kidney Disease Research Collaborative and the NHMRC Chronic Kidney Disease Centre of Research Excellence. Evan’s research is focused in two areas. Firstly, developing a chronic kidney disease Biobank into a bioresource for future use. Secondly, investigating progressive chronic kidney disease in terms of how it is defined, biomarkers of progression, and development of a predictive model that can more accurately predict progressive chronic kidney disease than current clinical options.

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