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Acute kidney injury (AKI) is a common and serious condition, currently diagnosed by functional biomarkers, such as serum creatinine measurements. Unfortunately, creatinine increase is a delayed and unreliable indicator of AKI. The lack of early biomarkers of structural kidney injury has hampered our ability to translate promising experimental therapies to human AKI. The recent discovery, translation and validation of neutrophil gelatinase-associated lipocalin (NGAL), possibly the most promising novel AKI biomarker, is reviewed here. NGAL may be measured by several methods both in plasma and urine for the early diagnosis of AKI and for the prediction of clinical outcomes, such as dialysis requirement and mortality, in several common clinical scenarios, including in the intensive care unit, cardiac surgery and renal damage due the exposition to toxic agent and drugs, and renal transplantation. Furthermore, the predictive properties of NGAL, may play a critical role in expediting the drug development process. A systematic review of literature data indicates that further studies are necessary to establish accurate reference population values according to age, gender and ethnicity, as well as reliable and specific decisional values concerning the more common clinical settings related to AKI. Furthermore, proper randomized clinical trials on renal and systemic outcomes comparing the use of NGAL vs. standard clinical practice are still lacking and accurate cost-benefit and/or cost-utility analyses for NGAL as biomarker of AKI are also needed. However, it is important to note that NGAL, in the absence of diagnostic increases in serum creatinine, is able to detect some patients affected by subclinical AKI who have an increased risk of adverse outcomes. These results also suggest that the concept and definition of AKI might need to be reassessed.

clinical chemistry and laboratory medicine (cclm)

Neutrophil gelatinase-associated lipocalin (NGAL) as biomarker of acute kidney injury: a review of the laboratory characteristics and clinical evidences