DKK3 as a potential novel biomarker in patients with autosomal polycystic kidney disease

DKK3 作为常染色体多囊肾病患者的潜在新型生物标志物

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作者:Sita Arjune, Martin R Späth, Simon Oehm, Polina Todorova, Stefan J Schunk, Katharina Lettenmeier, Seung-Hun Chon, Malte P Bartram, Philipp Antczak, Franziska Grundmann, Danilo Fliser, Roman-Ulrich Müller

Conclusion

uDKK3 shows a clear correlation with the Mayo classification in patients with ADPKD. uDKK3 levels correlated with kidney function, which could indicate that uDKK3 also predicts a disproportionate loss of renal function in this collective. Interestingly, we found an interaction between copeptin and uDKK3 in our prediction models and the best model containing both variables and their interaction term resulted in a fairly good explanation of variance in eGFR slope compared with previous models. Considering the limited number of patients in these analyses, future studies will be required to confirm the results. Nonetheless, uDKK3 appears to be an attractive candidate to improve outcome prediction of ADPKD in the future.

Methods

In 184 ADPKD patients from the AD(H)PKD registry and 47 healthy controls, the urinary DKK3 (uDKK3) levels were determined using ELISA. Multiple linear regression was used to examine the potential of these values in outcome prediction.

Results

ADPKD patients showed significantly higher uDKK3 values compared with the controls (mean 1970 ± 5287 vs 112 ± 134.7 pg/mg creatinine). Furthermore, there was a steady increase in uDKK3 with an increase in the Mayo class (A/B 1262 ± 2315 vs class D/E 3104 ± 7627 pg/mg creatinine), the best-established biomarker of progression in ADPKD. uDKK3 also correlated with estimated glomerular filtration rate (eGFR). Patients with PKD1 mutations show higher uDKK3 levels compared with PKD2 patients (PKD1: 2304 ± 5119; PKD2: 506.6 ± 526.8 pg/mg creatinine). Univariate linear regression showed uDKK3 as a significant predictor of future eGFR slope estimation. In multiple linear regression this effect was not significant in models also containing height-adjusted total kidney volume and/or eGFR. However, adding both copeptin levels and the interaction term between copeptin and uDKK3 to the model resulted in a significant predictive value of all these three variables and the highest R2 of all models examined (∼0.5).

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