Is serum copeptin a modifiable biomarker in autosomal dominant polycystic kidney disease?

Abstract

The availability of disease-modifying drugs for the management of autosomal dominant polycystic kidney disease (ADPKD) has accelerated the need to accurately predict renal prognosis and/or treatment response in this condition. Arginine vasopressin (AVP) is a critical determinant of postnatal kidney cyst growth in ADPKD. Copeptin (the C-terminal glycoprotein of the precursor AVP peptide) is an accurate surrogate marker of AVP release that is stable and easily measured by immunoassay. Cohort studies show that serum copeptin is correlated with disease severity in ADPKD, and predicts future renal events [decline in renal function and increase in total kidney volume (TKV)]. However, serum copeptin is strongly correlated with creatinine, and its additional value as a prognostic biomarker over estimated glomerular filtration rate and TKV is not certain. It has also been suggested that copeptin could be a predictive biomarker to select ADPKD patients who are most likely to benefit from AVP-modifying therapies, but prospective data to validate this assumption are required. In this regard, long-term randomised clinical trials evaluating the effect of prescribed water intake on renal cyst growth may contribute to addressing this hypothesis. In conclusion, although serum copeptin is aligned with the basic pathogenesis of ADPKD, further rigorous studies are needed to define if it will contribute to enabling the delivery of personalised care in ADPKD.

Biomarkers in Stress Related Diseases/Disorders: Diagnostic, Prognostic, and Therapeutic Values

“…There is a rise in the levels of copeptin in cardiovascular shock (Kristyagita and Siswanto, 2015). In recent times though copeptin is diagnostic biomarker for kidney (Tasneem et al, 2018) and heart (Berezin, 2018; Xu L. et al, 2018) related ailments mainly, but in future has good scope for evaluation of other systems or diseases as these systems are interdependent and affect one another, e.g., cardio-pulmonary or hepato-renal disorders. It has found clinical applications in acute myocardial infarction (Ay et al, 2017; Aydin et al, 2019), polycystic kidney disease (Tasneem et al, 2018), insulin resistance and metabolic syndrome (Saleem et al, 2009).…”

Attuali applicazioni della determinazione dei livelli plasmatici di copeptina in contesti non-endocrinologici

“…I livelli circolanti di AVP potrebbero, peraltro, risultare cronicamente elevati in questi pazienti, in maniera proporzionale allo stato di avanzamento della malattia, a causa del sovvertimento dell'architettura midollare renale, con conseguente ridotta capacità di riassorbimento di acqua libera da soluti dalle preurine. Purtuttavia, ad oggi non vi sono evidenze che copeptina possa rappresentare un biomarker diagnostico di tale condizione, in quanto solo lievemente elevata in questi pazienti e più in generale non chiaramente associata a una specifica causa sottostante di CKD (Tabella 2) [19]. La caratterizzazione del glicopeptide come marker prognostico di rapida progressione (definita come aumento del total kidney volume, TKV, e riduzione dell'eGFR), sembra invece confermata da studi a lungo termine condotti su ampie popolazioni, anche tenendo conto di possibili fattori interferenti [20].…”

Unmet needs and challenges for follow-up and treatment of autosomal dominant polycystic kidney disease: the paediatric perspective

“…In adult populations, several indicators for disease progression have been described: age (e.g. age at diagnosis) [44], male sex [45], LBW [46], race [47], BMI [48], BSA [49], PKD genotype [38], other ciliopathy genes [50], high HtTKV [51], low RBF [52], hypertension [44], urologic event [44], chronic asymptomatic pyuria [53], hernia [54], (e)GFR [52], proteinuria [55], cholesterol [49], urinary sodium excretion [49], urine osmolality [49], uric acid [56], thrombocyte count [57], copeptin [58], plasma ADH [59], MCP-1 [60], high protein intake [49], low water intake [61] and smoking [54]. These indicators gave rise to the development of different prediction models [41–43].…”