Danno renale acuto e biomarcatori diagnostici precoci: una revisione narrativa della letteratura

Abstract

Introduzione. Il danno renale acuto (AKI) è una condizione grave caratterizzata da un’improvvisa compromissione della funzione renale nei pazienti ospedalizzati, in particolare in terapia intensiva, ed è associato a elevata morbilità e mortalità. Gli attuali metodi diagnostici, basati sulle variazioni della creatinina sierica (sCr) e della diuresi, sono influenzati da numerosi fattori confondenti. L’obiettivo di questo studio è indagare il ruolo di biomarcatori specifici, in particolare NGAL, Cistatina C, IL-18 e KIM-1, nella diagnosi di AKI.
Materiali e metodi. In questa revisione della letteratura è stata condotta una ricerca approfondita nelle banche dati PubMed, Scopus e Google Scholar per valutare il potenziale diagnostico precoce di NGAL, Cistatina C, IL-18 e KIM-1 nei pazienti con AKI.
Biomarcatori. La lipocalina associata alla gelatinasi dei neutrofili (NGAL), sia sierica che urinaria, ha mostrato un aumento poco dopo l’insorgenza dell’AKI, diverse ore prima dell’incremento della sCr, permettendo di distinguere l’AKI dalla malattia renale cronica e dall’azotemia prerenale. La Cistatina C (CysC), una proteina prodotta e filtrata costantemente, è stata identificata come un biomarcatore affidabile per l’AKI, sebbene il suo costo elevato ne limiti l’utilizzo. L’interleuchina-18 (IL-18), una citochina pro-infiammatoria, ha mostrato un potenziale diagnostico, in particolare nei pazienti critici e dopo chirurgia cardiovascolare, anche se i risultati sulla sua capacità predittiva sono risultati eterogenei. La Kidney Injury Molecule-1 (KIM-1), una proteina rilasciata nelle urine in seguito a danno tubulare prossimale, ha dimostrato elevata sensibilità e specificità nelle fasi precoci dell’AKI, ed è stata inoltre associata a diverse patologie renali.
Conclusioni. I nuovi biomarcatori (NGAL, CysC, IL-18 e KIM-1) consentono una diagnosi più precoce e accurata dell’AKI rispetto ai metodi tradizionali in diversi contesti clinici. Sono tuttavia necessari ulteriori studi per integrare pienamente queste promettenti molecole nella pratica clinica quotidiana.

Parole chiave: danno renale acuto, biomarcatore, NGAL, cistatina C, IL-18, KIM-1

Ci spiace, ma questo articolo è disponibile soltanto in inglese.

Introduction

Acute kidney injury (AKI) is a common syndrome affecting up to one third of hospitalized patients with high prevalence among the intensive care unit patients and the older individuals and a high mortality [1, 2]. The term AKI describes a sudden impairment of the renal function and is characterized by increased serum creatinine with or without decreased urine output developing over a period of up to a week [24].  Historically, this pathological condition was first mentioned in the early 19th century, while the term acute renal failure was introduced in 1951. For the past two decades, the term acute kidney injury has been used to characterize a wide range of kidney diseases [2].

The first classification system of AKI was the RIFLE system, where R=Risk, I=Injury, F=Failure, L=Loss, E=End-stage disease, using as criteria the respective increase in the levels of serum creatinine and the respective reduction of urine output with or without a decreased GFR (Glomerular Filtration Rate) [5, 6]. As new classifications, AKIN-Acute Kidney Injury Network and KDIGO-Kidney Disease Improving Global Outcomes were developed, GFR was no longer evaluated while the increase of the serum creatinine levels and the decrease of the urine output remained the main criteria for the diagnosis of the acute kidney injury [68].

Acute kidney disease is associated with a variety of complications including metabolic abnormalities, pulmonary edema, cardiovascular and long-term renal complications such as the development of chronic kidney disease, while the gastrointestinal, the immune and the nervous systems are also affected [912]. Age, chronic kidney disease, diabetes mellitus, hypertension, cardiovascular diseases are not only risk factors for the development of acute kidney injury but also affect the prognosis of these patients along with the duration and severity of the acute kidney disease among others [12].

 

Pathophysiology of AKI

AKI can result from multiple causes, which are classified according to their pathophysiological mechanism into pre-renal, intra-renal, and post-renal. Among pre-renal causes, the most common are those characterized by reduced renal blood flow or renal ischemia; in intra-renal causes, damage to the renal parenchyma is usually observed; post-renal causes are characterized by an obstruction of the urinary system [3, 9, 1315] (Table 1).

Pre-renal

Decreased renal blood flow

– Sepsis, hypovolemia, shock

– Cardiorenal syndrome

– Hepatorenal syndrome

– Major surgical procedure

– Abdominal compartment syndrome

– Drugs including antihypertensives, contrast media and NSAIDs

– Hypercalcemia

Intra-renal

– Sepsis, systemic infections

– Vascular and blood diseases

– Autoimmune diseases

– Renal diseases including acute interstitial nephritis and rapidly progressive glomerulonephritis

– Rhabdomyolysis

– Tubular necrosis due to ischemia-prolonged low blood pressure

– Acute graft rejection

– Drugs including antibiotics, chemotherapy drugs, contrast media and heavy metals

– Cancer immunotherapy

Post-renal

– Benign prostatic hyperplasia

– Nephrolithiasis, blood clots

– Neoplasms of the genitourinary tract

– Fibrosis (radiation-induced, retroperitoneal)

Table 1. Common causes of AKI.

AKI due to surgery, heart diseases or administration of specific medications (Table 1) is more common in populations with a higher income compared to lower income populations, where kidney injury is mainly caused by dehydration, hypotension, infections including sepsis, venomous animal bites and in relatively rare cases as a complication of pregnancy [16].

In cases associated with acute sepsis, the acute kidney injury can be caused by inflammation, hemodynamic, and metabolic alterations [4, 17]. Of note, these factors, as well as mechanical and various other factors including specific medications and neurohormonal alterations, play a significant role in the development of acute kidney injury following a surgical procedure [8, 18, 19]. Neurohormonal alterations are a significant cause of AKI also in patients with hepatorenal syndrome as well as in patients with cardiorenal syndrome, while hemodynamic changes, inflammation and nephrotoxic drugs combined with chronic kidney disease are implicated as well [14].

Prolonged vasoconstriction and direct renal cellular damage due to iodinated contrast media can lead to acute kidney injury especially in patients suffering from diabetes mellitus and chronic kidney disease. These cases are characterized by a prolonged detection and action of the contrast media within the urinary system resulting in an impaired kidney function [20].

 

Diagnosis of AKI

As mentioned above, the current classifications are based on increased serum creatinine levels and/or a decreased urine output for the diagnosis of acute kidney injury. However, the serum creatinine levels can rise at a slow rate after AKI, and they are also affected by various factors such as diet, age, sex, medication, body muscle volume, hypervolemia, sepsis, rhabdomyolysis, chronic kidney disease, while various technical factors can affect the evaluation of the creatinine levels in the laboratory [6, 13]. Consequently, serum creatinine levels do not always represent accurately the GFR and cannot clarify the cause of the acute kidney injury, while some drugs can affect the prognostic potential of this marker [21].

Specific acute situations, such as surgery, severe pain or hemodynamic instability can result in a decreased urine output which is associated with a lower survival rate, while the duration of this decrease is essential for the prognosis of the patients suffering from acute kidney injury [6, 13, 18].

The management includes fluid administration, vasoactive drugs, withdrawal of the nephrotoxic substances, diuretics, management of the metabolic alterations, while in more severe cases renal replacement therapy may be required [9, 22].

The term biomarker is used to describe a measurable marker that characterizes a biological process as well as a pathological condition or response to treatment [23]. Various biomarkers both in serum and urine have been evaluated to contribute to an earlier diagnosis and as prognostic factors of acute kidney injury [24]. These biomarkers could indicate kidney damage, stress, or an impaired kidney function [22]. In the current literature, the most studied biomarkers for the early diagnosis of AKI are the Neutrophil Gelatinase-associated Lipocalin (NGAL), the Cystatin C, the Interleukin-18 (IL-18), and the Kidney Injury Molecule-1 (KIM-1). To summarize all available data regarding the utilization of the biomarkers mentioned above in early AKI diagnosis, as well as their efficacy compared to conventional biomarkers, we conducted a literature review of all available studies on these molecules. With this report, we aim to draw conclusions from the available literature regarding the use of these biomarkers in early AKI diagnosis.

 

Materials and methods

Study design

To address the research question, we designed and conducted this literature review to investigate any relationship of selected biomarkers to an early diagnosis of AKI onset. The research question was defined using the PICO (Population/Participants, Intervention/Investigation, Comparison/Comparator, Outcomes) framework:

  • Population: All patients diagnosed with Acute Kidney Injury
  • Investigation: Use of diagnostic biomarkers (NGAL, Cystatin C, IL-18, KIM-1)
  • Comparison: No comparator investigation
  • Outcomes: Early diagnosis of Acute Kidney Injury.

Eligibility criteria 

For this literature review, original research articles, reviews including meta-analyses, and clinical trials were included. Only articles in English language and with a full text available were selected.

Search strategy

A thorough search of the MEDLINE database via PubMed, Scopus and Google Scholar was conducted, from January 1990 to the last search date, September 16th, 2024, using the algorithms: ((acute kidney injury) OR (aki)) AND ((diagnostic biomarkers) OR (NGAL) OR (cystatin c) OR (IL-18) OR (KIM-1)). Two independent reviewers (GG, AK) performed the title and abstract screening and then assessed the studies for eligibility through full text evaluation of the articles according to the eligibility criteria mentioned above. Disagreements were addressed by a third independent author (AP).

 

Neutrophil gelatinase-associated lipocalin (NGAL)

Human NGAL, also known as lipocalin-2, is a protein with a specific weight of 25-kDa that is secreted mostly by immune cells such as neutrophils, but it can also be found in numerous human tissues, such as salivary glands, uterus, prostate, trachea, lungs, stomach, colon and kidneys [25, 26].

In the kidneys, NGAL is bound to neutrophil cell gelatinase and is released from the distal tube [27]. The molecule then is filtered through the glomerular membrane and is reabsorbed in the proximal tubule of the kidney while the NGAL quantities observed in urine are caused by proximal tubular damage or originates from their up-regulated synthesis in the distal part of the nephron, especially in the ascending limb and Henle’s loop, and in the collecting duct [28].

NGAL is known to be a siderophoric protein that plays a role in regulating iron activity [29]. NGAL quantities that are not bound to iron can interact with the cell surface receptors resulting in extracellular transfer of intracellular iron [30]. Studies have shown that NGAL interferes in binding iron together with a metabolic product called catechol and creates complexes [31] which enhance microbial growth [32] or mediate oxidative damage [33].

Also, NGAL plays a significant role as an acute phase protein and takes part in various antibacterial immune processes. Inflammatory cytokines induce NGAL expression in neutrophils, epithelial cells, or hepatocytes [34]. The injury of epithelial cells in the intestines [35], stomach, liver [36], or lungs during infections results in an increase in serum NGAL (sNGAL) concentration levels. Additionally, sNGAL levels are higher in patients with septic shock and sepsis-related organ failure compared to those with a milder course of sepsis as shown by a study [37]. Research has shown that NGAL is associated with epithelia and in particular its production increases greatly and sharply when epithelial tissues are damaged, a function investigated by many studies in correlation with early diagnosis of AKI. The previous study [38] also reported that while NGAL is expressed normally at very low concentrations in physiologic conditions, its levels increase sharply after tissue damage, thus categorizing the molecule as a defense protein of the host organism. In the same study, it is reported that NGAL rising above a certain level indicates kidney injury about 24-48 hours before serum creatinine (sCr) does [38]. Serum NGAL can be detected within two hours of AKI onset, with a concentration peak after 6 hours while sNGAL levels remained increased for approximately five days after AKI onset [39].

Another study [31] showed that in 30 patients out of 635 who developed AKI, NGAL in urine was elevated much earlier and in higher levels than sCr. In addition, urinary NGAL (uNGAL) appears as a suitable marker to distinguish AKI from chronic renal failure (CKD) and prerenal azotemia given its acute level increase in AKI compared to CKD and prerenal azotemia, in which no increase was observed [31]. An experimental study in animals suggested that sNGAL can predict early diagnosis of cisplatin-induced AKI accurately but is less useful in later stages compared to blood urea nitrogen (BUN) and sCr [40]. Moreover, another study [41] reported that in 45 patients who developed AKI (out of 77 in the study), uNGAL and urine cystatin C (uCysC) increased rapidly in several cases and remained elevated after the acute phase. The utility of sNGAL had also emerged, contributing positively in diagnosing 100% of patients who developed AKI later [41]. The above three indicators increased approximately 6-24 hours before sCr levels exceeded 0.3mg/dl, while uNGAL in some cases immediately increased significantly and within 24 hours of admission and predicted risk of death or the need for hemodialysis [41]. The NGAL seems to have many prospects to be established as an early biomarker of AKI, as it has been reviewed in various applications including a study in children treated with cardiopulmonary bypass (CPB) [42]. Out of the 196 patients who participated in the research and had gone through CPB, 99 later developed AKI. In this study, high levels of creatinine were reported only 2-3 days after CPB, while NGAL increased 15-fold 2 hours and 25-fold 4-6 hours after the surgical procedure [42].

NGAL has been found to be an independent predictor of CKD progression [43], also demonstrating promise as a marker determining the iron status in CKD instead of serum ferritin, including patients who require renal replacement therapy [44].

NGAL performed significantly in a study linking AKI and cardiac failure [45]. Specifically, in 203 patients, of whom 107 already had chronic renal insufficiency and 96 demonstrated normal renal function, 78 developed AKI. In those with AKI (the threshold for sNGAL was 134 ng/ml) sNGAL increased significantly, while on the same metrics cystatin C (CysC) did not perform adequately. Notably, the performance of natriuretic peptide type B (BNP) was remarkable in all cases of AKI. [45]. As a side note, the previous study indicated the potential usefulness of sNGAL in the early diagnosis of AKI in the setting of heart failure, and showed promise as a marker of renal function in the manifestation of cardiorenal syndrome, while CysC did not contribute to the diagnosis of AKI but showed a relevance with CKD and possibly laid the foundations as an indicator of CKD, as well as of decreased kidney function (impaired GFR) [45]. Promising results for NGAL’s efficiency for the diagnosis and management of AKI and hepatorenal syndrome have also been shown [46, 47].

In a 2023 study by Gupta et al. in trauma patients in ICU, the cut-off values of sNGAL and uNGAL were determined as 122-125 ng/mL and 16 ng/mL, respectively, with sNGAL demonstrating greater prognostic potential in this study [48]. Similarly, Saha et al. in a 2025 study on the diagnosis of AKI in the setting of acute liver failure (ALF), reported that the cut-off values for sNGAL were 129 ng/mL in AKI without ALF, while in patients with ALF and AKI, sNGAL showed a less significant increase [49]. It is also worth mentioning the prospective study by Katz-Greenberg et al. in 2022, where uNGAL was measured in patients with possible admission to the ICU and a diagnosis of AKI. uNGAL increased up to 24-fold depending on the stage of injury, according to the KDIGO staging, where in stage I a mean value of 1255 ng/ml was measured, while a significant increase was also measured in patients needing RRT [50].

Of note, it should be underlined that studies on NGAL in AKI populations are relatively limited in size and may be affected by a number of conditions, such as pre-existing kidney disease and systemic or urinary tract infections [51].

Of course, there are also elements which show that there is still a long way to go from NGAL’s establishment as an indicator for the diagnosis of AKI, such as trials where NGAL had mediocre statistical results and failed at a rate to contribute to diagnosis of AKI [24] or demonstrated limited usefulness, especially when systemic inflammation occurs concurrently with AKI [52].

 

Cystatin C

Cystatin C (CysC) is a 13-kDa cysteine proteinase inhibitor protein with an important role in the catabolism of intracellular peptides and proteins and is produced by all nucleated cells at a constant rate [53]. As blood is filtered by the kidneys, CysC is freely filtered by the glomerulus and like glucose and other substances, is almost completely reabsorbed and catabolized in the proximal tubule [54]. It is not affected by extrarenal factors such as gender, age, race or muscle mass and thus is considered as an independent marker of renal function, compared to sCr [55]. CysC is considered a reliable biomarker of AKI because it is filtered by 99%, is continuously produced and released into plasma and has been found to be more accurate than serum creatinine in many different patient populations [54]. A study showed that in patients with diagnosed AKI, the levels of cystatin C excreted in urine (uCysC) can be used to predispose the need for possible kidney transplantation [56], while another study by the same research team showed that with the fall of estimated GFR score due to AKI, CysC levels increased quite quickly and earlier than sCr. Specifically, in cases of R, I and F from the classic RIFLE ranking system for AKI, CysC increased to a certain level 1.5 days prior to sCr and thus was characterized as not only more sensitive, but also more specific than sCr [56, 57]. Other potential roles for cystatin C include being an earlier marker for acute kidney injury, a superior marker of kidney transplant function, CVD (cardiovascular disease) risk and transplant failure [58].

CysC has been associated with progression to end stage renal disease and mortality in patients with diabetes [59], acute kidney injury [60], CKD [61, 62], and end-stage CKD on dialysis [63]. CysC has been shown to portray an important role as a biomarker in CKD classification and overall risk and mortality, as shown by a meta-analysis, where a significant difference in GFR estimation using CysC compared with sCr was observed in pre-end-stage CKD [64]. This correlation between CysC and risk and mortality in CKD and end-stage CKD has been shown by many other studies, always compared with sCr [65]. CysC has been found to be more accurate than serum creatinine in many different patient populations [54]. It depicts earlier, more subtle changes in kidney function, while further research and development is needed to improve its cost-effectiveness [66]. This is its most significant limitation, as the cost of CysC, approximately 10-fold of sCr, is considered a significant prohibitive factor for routine use in clinical practice [65] while regarding other restrictions, it can be affected by thyroid disease, obesity, systemic inflammation and steroid use [67]. Lastly, while GFR estimation using CysC can still fall victim to its non-GFR determinants, such as thyroid function and steroid use, its independent association with CVD, end-stage CKD, and all-cause mortality warrants CysC the possibility of becoming a reliable biomarker for these clinical conditions [66].

 

Interleukin-18

Interleukin 18 (IL-18) is a cytokine promoting inflammation produced by a variety of cells including renal tubular cells [68, 69]. IL-18 has a possible role as a biomarker of AKI as inflammation resulting in parenchymal injury is heavily involved in the pathogenesis of AKI [70], whereas its release is also associated with pre-renal AKI causes such as ischemia, activated by the enzyme caspase-1 and released in urine [71]. IL-18 promotes inflammation and ultimately is a factor that can contribute towards renal fibrosis after AKI, as reported in a renal injury mouse model study in 2022, where possible deficiency of IL-18 in renal tubular cells in mice prevented alterations indicative of further progression of AKI into chronic kidney disease [71]. Also, this pro-inflammatory cytokine is associated with the development and progression of diabetic nephropathy through a wide range of mechanisms [72, 73].

IL-18 levels in urine rise after cardiovascular surgery [7476] and in critically ill patients, such as septic patients, preceding the diagnosis of AKI [74, 75, 77, 78], while it is reported as both a reliable [69] and a poor [79] prognostic biomarker in these patients. Serum IL-18 levels in critically ill patients with AKI starting hemodialysis predicted the risk of death [80]. Promising results were also reported more in children and adolescents than adults with AKI in the above conditions (cardiovascular surgery, ICU patients), according to a meta-analysis [81]. Higher urinary IL-18 levels after heart surgery are associated with a longer duration of AKI [82], while serum IL-18 also increased within hours [83]. Urinary IL-18 levels were elevated earlier compared to serum creatinine in patients with AKI developing ARDS (acute respiratory distress syndrome) [74, 84, 85], as well as in patients receiving a kidney transplant, where high levels of urinary IL-18 were associated with delayed graft function [75, 84]. Urinary IL-18 in patients with cirrhosis could determine the cause of AKI, specifically for the diagnosis of acute tubular necrosis as well as predict mortality and progression of AKI [86]. The levels of urinary IL-18 were elevated within hours after liver transplantation combined with AKI as shown by a study [87], while another study showed that elevated plasma IL-18 levels post transplantation could also predict AKI [88].

Urinary IL-18 levels were elevated due to AKI in specific situations, such as cardiac catheterization [89], burn injuries [90] and urological interventions.

Serum IL-18 levels are higher in patients under hemodialysis compared to patients with AKI, while its levels rise with age [70]. Urinary IL-18 can also be elevated in autoimmune and inflammatory diseases [68, 84, 91], in various diseases of the urinary system and in diseases of the heart or the lungs [69].

Urinary IL-18 could not only detect AKI earlier compared to elevated serum creatinine levels, with better results in children than adults [68, 69], but also serve as a prognostic marker [92].

 

Kidney Injury Molecule-1 (KIM-1)

KIM-1 (Kidney Injury Molecule-1) or TIM-1 (T-cell immunoglobulin mucin receptor-1) or HAVCR 1 (Hepatitis A Virus Cellular Receptor 1) is a cellular membrane protein expressed in renal proximal tubular epithelial cells which under normal conditions is found at low levels in urine [93, 94]. Higher levels of this molecule are found after kidney injury as its extracellular portion is released into the urine [93, 94], with the contribution of MAPK (mitogen activated protein kinase) pathways [95]. KIM-1 function is associated with phagocytosis, tissue repair, fibrosis and inflammation, whereas increased levels of this biomarker are detected in the urine in a variety of renal diseases such as acute kidney injury, renal disease due to diabetes, chronic kidney disease, IgA nephropathy, lupus nephritis, polycystic renal disease, renal cell carcinoma as well as in patients with a transplanted kidney suffering from graft loss [9397]. KIM-1 appears to promote repair of the kidney tubular epithelial cells through the ERK/MAPK signaling pathway [98].

KIM-1 is an FDA approved biomarker of drug induced renal injury, demonstrating encouraging outcomes regarding its use in incidents of cisplatin-induced acute kidney injury [94], as high levels of KIM-1 can be detected prior to creatinine increase [99].

In a meta-analysis by Shao et al in 2014, urinary KIM-1 had a sensitivity of 74% and specificity of 86% for the diagnosis of AKI, demonstrating better results in patients undergoing heart surgery, particularly when urine levels of KIM-1 were measured 2-12 hours post-operatively [100]. Additionally, promising results have been reported in infants and children in the same study compared to adults, while comorbidities and various technical issues regarding the detection methodology could alter the urinary KIM-1 levels [100]. Urinary KIM-1 levels also increased in cases of AKI post heart valve replacement surgery [101]. Furthermore, KIM-1 has been reported to be a promising biomarker of kidney injury associated with chronic heart failure or myocardial infarction [102].

Urinary KIM-1 can predict acute kidney injury 12 hours after coronary angiography, albeit with a lower specificity and sensitivity than NGAL [103].

In renal biopsies, KIM-1 staining was proved to be a valuable marker of acute tubular injury while the KIM-1/serum creatinine ratio appears to be a valuable marker of renal function recovery post injury, respectively [104].

Urinary KIM-1 is a potential early biomarker in instances of acute kidney injury diagnosis due to sepsis, while persistent high levels of this marker could be indicative of a poor prognosis [105].

Significantly increased levels of urinary KIM-1 in type 2 diabetic patients with diabetic nephropathy were associated with a poor prognosis regarding the progression of the renal disease [106]. Also, in patients with type 1 diabetes, increased levels of urinary KIM-1 can precede albuminuria [94]. In renal biopsy specimens of patients with diabetic nephropathy, KIM-1 expression in the renal tubules was found to correlate positively with the associated GFR decline, a correlation that was dependent on the urinary protein-to-creatinine ratio [107].

Urine levels of KIM-1 24 hours post transplantation were reported to be predictive of the recipients’ renal graft function [108], while an association between perfusate levels of this biomarker and delayed graft function, as well as an impaired eGFR three years after the transplantation, were also found [109]. KIM-1 staining in specimens received from transplanted kidneys was reported to be a specific and sensitive marker of proximal tubular injury that is negatively associated with the presence of functional epithelial cells of the proximal tubule and eGFR [110, 111] and positively correlated with the serum creatinine and blood urea nitrogen levels [112].

Serum KIM-1 levels correlated positively with the severity of histological features (acute tubular necrosis, interstitial fibrosis-tubular atrophy) and negatively with eGFR in patients suffering from ANCA-associated (antineutrophil cytoplasmic antibodies-associated) vasculitis with glomerulonephritis [113].

KIM-1 is a promising biomarker because it is normally detected in low levels in urine, but after kidney injury, its levels rise rapidly and have been associated with the degree of the injury. Furthermore, the extracellular domain (ectodomain) of KIM-1 that is released into the urine, is stable at room temperature [97]. On the other hand, there are limited data from clinical studies, KIM-1 is frequently measured in combination with other biomarkers to improve its diagnostic utility, its levels in urine may increase hours after the injury, the detection process of this biomarker is expensive, thus rendering it difficult to access, while comorbidities and technical issues regarding the detection methodology could also affect the urinary KIM-1 levels [100, 101, 114].

 

Future perspectives

Acute kidney injury affects 30-50% of critically ill patients and is associated with increased mortality, longer ICU stay and risk of progression to chronic kidney disease, mainly due to surgical procedures, nephrotoxic drugs, electrolyte imbalances or sepsis [115]. In those patients, a prospective biomarker must possess the ability to detect early and accurately the tubular injury before the decline in renal function becomes clinically apparent, to offer specificity and sensitivity to the various causes of AKI (prerenal, renal, postrenal), to stratify high-risk patients, and to assist in the identification of patients with possible progression to CKD and RRT. Among the biomarkers presented in this review, the most prevalent biomarker performing consistently in the ICU setting, either in sepsis-related [116], trauma-related [48] or post cardiac surgery AKI [117], is the Neutrophil Gelatinase-Associated Lipocalin or NGAL, as mentioned extensively in the corresponding section. Additionally, as already mentioned, sNGAL’s threshold values are reported to be in the range of 120-200 ng/ml, as reported by another study [118]. As for uNGAL, cut-off values are not sufficiently defined, however a recent 2025 study by Strander et al. in postoperative patients in the ICU after cardiac surgery reported the threshold value of uNGAL at 150 ng/mL, with satisfactory predictive performance [117].

It is necessary to emphasize again the usefulness of NGAL in predicting the progression of AKI to CKD with the need for RRT [50]. NGAL was found to detect patients with progressively deteriorating AKI, as observed in the ELAIN randomized clinical trial in 2016 [119], while in another study, the STARRT-AKI in 2022, sNGAL ≥ 400 ng/mL along with a two-fold increase in serum creatinine and oliguria was used as guiding criterion predicting the early start of RRT [120]. As already mentioned, NGAL can be affected by various factors, such as systemic or urinary tract inflammation, pre-existing kidney diseases, timing of sampling, and fluid imbalances.

Therefore, considering the multiple applications of NGAL in patients with mild disease, in the ICU, but also in predicting a possible need for RRT, we suggest NGAL as the most mature and clinically applicable biomarker for utilization in critical care of those presented in this study.

 

Conclusions

Acute kidney injury (AKI) is a common clinical syndrome, primarily diagnosed based on serum creatinine levels and urine output, markers that are affected by various factors such as diet, age, sex, medication and body muscle volume. Current evidence suggests that various novel biomarkers may provide a better alternative, allowing for an earlier and more precise detection of AKI. The most prevalent and clinically applicable biomarker, Neutrophil Gelatinase-Associated Lipocalin or NGAL, has been shown by a large number of studies to be a strong predictor of AKI, mainly in intensive care, showing efficacy in diagnosing AKI of various causes and in predicting the need for RRT. Kidney Injury Molecule-1 appears as a sensitive marker of proximal tubular injury, with significant shifts in concentration occurring hours to days prior to serum creatinine elevation. Cystatin C appears to be a precise marker of deteriorating renal function (eGFR), is produced and filtered at a constant rate, but its high cost prevents it from being utilized frequently in the clinical setting. Lastly, Interleukin-18, a cytokine promoting inflammation, is associated with pre-renal and intra-renal causes of AKI and has demonstrated better results in children rather than adults.

Incorporating these molecules into clinical practice, especially NGAL, offers the clinicians early detection tools, aiding in AKI diagnosis and management. To fully explore their potential in improving clinical results and possibly preventing the progression to chronic kidney disease, future research must focus most importantly on evaluating the accuracy and function of these biomarkers in AKI patients with various existing comorbidities, as well as establishing lab-standardized threshold values.

 

Bibliography

  1. International Society of Nephrology. Acute Kidney Injury (AKI) TOOLKIT. Retrieved August 14, 2024, from https://www.theisn.org/initiatives/toolkits/acute-kidney-injury-aki-toolkit/.
  2. Makris K, Spanou L. Acute Kidney Injury: Definition, Pathophysiology and Clinical Phenotypes. Clin Biochem Rev. 2016 May;37(2):85-98. PMID: 28303073; PMCID: PMC5198510.
  3. Kellum JA, Romagnani P, Ashuntantang G, Ronco C, Zarbock A, Anders HJ. Acute kidney injury. Nat Rev Dis Primers. 2021;7(1):52. https://doi.org/10.1038/s41572-021-00284-z.
  4. Pickkers P, Darmon M, Hoste E, et al. Acute kidney injury in the critically ill: an updated review on pathophysiology and management. Intensive Care Med. 2021;47(8):835-850. https://doi.org/10.1007/s00134-021-06454-7.
  5. Hoste EA, Clermont G, Kersten A, et al. RIFLE criteria for acute kidney injury are associated with hospital mortality in critically ill patients: a cohort analysis. Crit Care. 2006;10(3):R73. https://doi.org/10.1186/cc4915.
  6. Thomas ME, Blaine C, Dawnay A, et al. The definition of acute kidney injury and its use in practice. Kidney Int. 2015;87(1):62-73. https://doi.org/10.1038/ki.2014.328.
  7. Ostermann M, Bellomo R, Burdmann EA, et al; Conference Participants. Controversies in acute kidney injury: conclusions from a Kidney Disease: Improving Global Outcomes (KDIGO) Conference. Kidney Int. 2020;98(2):294-309. https://doi.org/10.1016/j.kint.2020.04.020.
  8. Djordjević A, Šušak S, Velicki L, Antonič M. Acute Kidney Injury After Open-Heart Surgery Procedures. Acta Clin Croat. 2021;60(1):120-126. https://doi.org/10.20471/acc.2021.60.01.17.
  9. Goyal A, Daneshpajouhnejad P, Hashmi MF, Bashir K. Acute Kidney Injury. 2023 Nov 25. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2024 Jan–. PMID: 28722925.
  10. Uehara A, Shibagaki Y (2020). Chapter 11 Complication of Homeostasis (Electrolytes and Acid-Base). In: Y. Terada et al. (eds.), Acute Kidney Injury and Regenerative Medicine. Springer Nature Singapore. https://doi.org/10.1007/978-981-15-1108-0_11.
  11. Tada M, Hayashi H, Tsuboi N, Yuzawa Y (2020). Chapter 12 Volume Overload and Pulmonary Complications. In: Y. Terada et al. (eds.), Acute Kidney Injury and Regenerative Medicine. Springer Nature Singapore. https://doi.org/10.1007/978-981-15-1108-0_12.
  12. Furuichi K, Yamamura Y, Wada T (2020). Chapter 18 Short-Term and Long-Term Outcomes of AKI Patients. In: Y. Terada et al. (eds.), Acute Kidney Injury and Regenerative Medicine. Springer Nature Singapore. https://doi.org/10.1007/978-981-15-1108-0_18.
  13. Gameiro J, Fonseca JA, Outerelo C, Lopes JA. Acute Kidney Injury: From Diagnosis to Prevention and Treatment Strategies. J Clin Med. 2020;9(6):1704. https://doi.org/10.3390/jcm9061704.
  14. Ronco C, Bellomo R, Kellum JA. Acute kidney injury. Lancet. 2019;394(10212):1949-1964. https://doi.org/10.1016/S0140-6736(19)32563-2.
  15. Rahman M, Shad F, Smith MC. Acute kidney injury: a guide to diagnosis and management. Am Fam Physician. 2012 Oct 1;86(7):631-9. PMID: 23062091.
  16. Feehally J. The ISN 0by25 Global Snapshot Study. Ann Nutr Metab. 2016;68 Suppl 2:29-31. https://doi.org/10.1159/000446202.
  17. Peerapornratana S, Manrique-Caballero CL, Gómez H, Kellum JA. Acute kidney injury from sepsis: current concepts, epidemiology, pathophysiology, prevention and treatment. Kidney Int. 2019;96(5):1083-1099. https://doi.org/10.1016/j.kint.2019.05.026.
  18. Boyer N, Eldridge J, Prowle JR, Forni LG. Postoperative Acute Kidney Injury. Clin J Am Soc Nephrol. 2022;17(10):1535-1545. https://doi.org/10.2215/CJN.16541221.
  19. Gumbert SD, Kork F, Jackson ML, et al. Perioperative Acute Kidney Injury. Anesthesiology. 2020;132(1):180-204. https://doi.org/10.1097/ALN.0000000000002968.
  20. McCullough PA, Choi JP, Feghali GA, Schussler JM, Stoler RM, Vallabahn RC, Mehta A. Contrast-Induced Acute Kidney Injury. J Am Coll Cardiol. 2016;68(13):1465-1473. https://doi.org/10.1016/j.jacc.2016.05.099.
  21. Bufkin KB, Karim ZA, Silva J. Review of the limitations of current biomarkers in acute kidney injury clinical practices. SAGE Open Med. 2024;12:20503121241228446. https://doi.org/10.1177/20503121241228446.
  22. Yoon SY, Kim JS, Jeong KH, Kim SK. Acute Kidney Injury: Biomarker-Guided Diagnosis and Management. Medicina (Kaunas). 2022;58(3):340. https://doi.org/10.3390/medicina58030340.
  23. Parikh CR, Moledina DG, Coca SG, Thiessen-Philbrook HR, Garg AX. Application of new acute kidney injury biomarkers in human randomized controlled trials. Kidney Int. 2016 ;89(6):1372-9. https://doi.org/10.1016/j.kint.2016.02.027.
  24. Coca SG, Yalavarthy R, Concato J, Parikh CR. Biomarkers for the diagnosis and risk stratification of acute kidney injury: a systematic review. Kidney Int. 2008;73(9):1008-16. https://doi.org/10.1038/sj.ki.5002729.
  25. Liu F, Yang H, Chen H, Zhang M, Ma Q. High expression of neutrophil gelatinase-associated lipocalin (NGAL) in the kidney proximal tubules of diabetic rats. Adv Med Sci. 2015 ;60(1):133-8. https://doi.org/10.1016/j.advms.2015.01.001.
  26. Jaberi SA, Cohen A, D’Souza C, et al. Lipocalin-2: Structure, function, distribution and role in metabolic disorders. Biomed Pharmacother. 2021;142:112002. https://doi.org/10.1016/j.biopha.2021.112002.
  27. Wen Y, Parikh CR. Current concepts and advances in biomarkers of acute kidney injury. Crit Rev Clin Lab Sci. 2021;58(5):354-368. https://doi.org/10.1080/10408363.2021.1879000.
  28. Helanova K, Spinar J, Parenica J. Diagnostic and prognostic utility of neutrophil gelatinase-associated lipocalin (NGAL) in patients with cardiovascular diseases–review. Kidney Blood Press Res. 2014;39(6):623-9. https://doi.org/10.1159/000368474.
  29. Xiao X, Yeoh BS, Vijay-Kumar M. Lipocalin 2: An Emerging Player in Iron Homeostasis and Inflammation. Annu Rev Nutr. 2017;37:103-130. https://doi.org/10.1146/annurev-nutr-071816-064559.
  30. Marakala V. Neutrophil gelatinase-associated lipocalin (NGAL) in kidney injury – A systematic review. Clin Chim Acta. 2022;536:135-141. https://doi.org/10.1016/j.cca.2022.08.029.
  31. Bao G, Clifton M, Hoette TM, et al. Iron traffics in circulation bound to a siderocalin (Ngal)-catechol complex. Nat Chem Biol. 2010;6(8):602-9. https://doi.org/10.1038/nchembio.402.
  32. Flo TH, Smith KD, Sato S, Rodriguez DJ, Holmes MA, Strong RK, Akira S, Aderem A. Lipocalin 2 mediates an innate immune response to bacterial infection by sequestrating iron. Nature. 2004;432(7019):917-21. https://doi.org/10.1038/nature03104.
  33. Nickolas TL, O’Rourke MJ, Yang J, et al. Sensitivity and specificity of a single emergency department measurement of urinary neutrophil gelatinase-associated lipocalin for diagnosing acute kidney injury. Ann Intern Med. 2008;148(11):810-9. https://doi.org/10.7326/0003-4819-148-11-200806030-00003.
  34. Luchtefeld M, Preuss C, Rühle F, et al. Gp130-dependent release of acute phase proteins is linked to the activation of innate immune signaling pathways. PLoS One. 2011;6(5):e19427. https://doi.org/10.1371/journal.pone.0019427.
  35. Yoo do Y, Ko SH, Jung J, et al. Bacteroides fragilis enterotoxin upregulates lipocalin-2 expression in intestinal epithelial cells. Lab Invest. 2013;93(4):384-96. https://doi.org/10.1038/labinvest.2013.1.
  36. Xu MJ, Feng D, Wu H, et al. Liver is the major source of elevated serum lipocalin-2 levels after bacterial infection or partial hepatectomy: a critical role for IL-6/STAT3. 2015;61(2):692-702. https://doi.org/10.1002/hep.27447.
  37. Lentini P, de Cal M, Clementi A, D’Angelo A, Ronco C. Sepsis and AKI in ICU Patients: The Role of Plasma Biomarkers. Crit Care Res Pract. 2012;2012:856401. https://doi.org/10.1155/2012/856401.
  38. Zappitelli M, Washburn KK, Arikan AA, et al. Urine neutrophil gelatinase-associated lipocalin is an early marker of acute kidney injury in critically ill children: a prospective cohort study. Crit Care. 2007;11(4):R84. https://doi.org/10.1186/cc6089.
  39. Wasung ME, Chawla LS, Madero M. Biomarkers of renal function, which and when? Clin Chim Acta. 2015;438:350-7. https://doi.org/10.1016/j.cca.2014.08.039.
  40. Wang W, Li Z, Chen Y, Wu H, Zhang S, Chen X. Prediction Value of Serum NGAL in the Diagnosis and Prognosis of Experimental Acute and Chronic Kidney Injuries. 2020;10(7):981. https://doi.org/10.3390/biom10070981
  41. Ralib AM, Pickering JW, Shaw GM, et al. The clinical utility window for acute kidney injury biomarkers in the critically ill. Crit Care. 2014;18(6):601. https://doi.org/10.1186/s13054-014-0601-2
  42. Bennett M, Dent CL, Ma Q, Dastrala S, et al. Urine NGAL predicts severity of acute kidney injury after cardiac surgery: a prospective study. Clin J Am Soc Nephrol. 2008 ;3(3):665-73. https://doi.org/10.2215/CJN.04010907
  43. Bolignano D, Lacquaniti A, Coppolino G, Donato V, et al. Neutrophil gelatinase-associated lipocalin (NGAL) and progression of chronic kidney disease. Clin J Am Soc Nephrol. 2009;4(2):337-44. https://doi.org/10.2215/CJN.03530708
  44. Tomasz G, Ewa W, Jolanta M. Biomarkers of iron metabolism in chronic kidney disease. Int Urol Nephrol. 2021;53(5):935-944. https://doi.org/10.1007/s11255-020-02663-z
  45. Palazzuoli A, Ruocco G, Pellegrini M, et al. Comparison of Neutrophil Gelatinase-Associated Lipocalin Versus B-Type Natriuretic Peptide and Cystatin C to Predict Early Acute Kidney Injury and Outcome in Patients With Acute Heart Failure. Am J Cardiol. 2015;116(1):104-11. https://doi.org/10.1016/j.amjcard.2015.03.043
  46. Allegretti AS, Solà E, Ginès P. Clinical Application of Kidney Biomarkers in Cirrhosis. Am J Kidney Dis. 2020 Nov;76(5):710-719. https://doi.org/10.1053/j.ajkd.2020.03.016
  47. Gambino C, Piano S, Stenico M, et al. Diagnostic and prognostic performance of urinary neutrophil gelatinase-associated lipocalin in patients with cirrhosis and acute kidney injury. Hepatology. 2023;77(5):1630-1638. https://doi.org/10.1002/hep.32799
  48. Gupta B, Tiwari P, Subramanian A, et al. Evaluation of plasma and urine neutrophil gelatinase-associated lipocalin (NGAL) as an early diagnostic marker of acute kidney injury (AKI) in critically ill trauma patients. J Anaesthesiol Clin Pharmacol. 2023;39(2):292-301. https://doi.org/10.4103/joacp.joacp_284_21
  49. Saha R, Sharma S, Mondal A, et al. Evaluation of Acute Kidney Injury (AKI) Biomarkers FABP1, NGAL, Cystatin C and IL-18 in an Indian Cohort of Hospitalized Acute-on-chronic Liver Failure (ACLF) Patients. J Clin Exp Hepatol. 2025;15(3):102491. https://doi.org/10.1016/j.jceh.2024.102491
  50. Katz-Greenberg G, Malinchoc M, Broyles DL, et al. Urinary Neutrophil Gelatinase-Associated Lipocalin Predicts Intensive Care Unit Admission Diagnosis: A Prospective Cohort Study. Kidney360. 2022;3(9):1502-1510. Published 2022 Jul 13. https://doi.org/10.34067/KID.0001492022
  51. Devarajan P. Neutrophil gelatinase-associated lipocalin (NGAL): a new marker of kidney disease. Scand J Clin Lab Invest Suppl. 2008;241:89-94. https://doi.org/10.1080/00365510802150158
  52. Devarajan P. Review: neutrophil gelatinase-associated lipocalin: a troponin-like biomarker for human acute kidney injury. Nephrology (Carlton). 2010;15(4):419-28. https://doi.org/10.1111/j.1440-1797.2010.01317.x
  53. Abrahamson M, Olafsson I, Palsdottir A, et al. Structure and expression of the human cystatin C gene. Biochem J. 1990;268(2):287-94. https://doi.org/10.1042/bj2680287
  54. Zhang Z, Lu B, Sheng X, Jin N. Cystatin C in prediction of acute kidney injury: a systemic review and meta-analysis. Am J Kidney Dis. 2011;58(3):356-65. https://doi.org/10.1053/j.ajkd.2011.02.389. Erratum in: Am J Kidney Dis. 2012 ;59(4):590-2. PMID: 21601330
  55. Levey AS, Inker LA, Coresh J. GFR estimation: from physiology to public health. Am J Kidney Dis. 2014;63(5):820-34. https://doi.org/10.1053/j.ajkd.2013.12.006
  56. Herget-Rosenthal S, Poppen D, Hüsing J, et al. Prognostic value of tubular proteinuria and enzymuria in nonoliguric acute tubular necrosis. Clin Chem. 2004;50(3):552-8. https://doi.org/10.1373/clinchem.2003.027763
  57. Herget-Rosenthal S, Marggraf G, Hüsing J, et al. Early detection of acute renal failure by serum cystatin C. Kidney Int. 2004 ;66(3):1115-22. https://doi.org/10.1111/j.1523-1755.2004.00861.x
  58. Moreira CL, Cunha L, Correia S, Silva F, et al. Does Cystatin C have a role as metabolic surrogate in peritoneal dialysis beyond its association with residual renal function? J Bras Nefrol. 2020;42(1):31-37. https://doi.org/10.1590/2175-8239-JBN-2019-0007
  59. Ide H, Iwase M, Fujii H, Ohkuma T, et al. Comparison of cystatin C- and creatinine-based estimated glomerular filtration rates for predicting all-cause mortality in Japanese patients with type 2 diabetes: the Fukuoka Diabetes Registry. Clin Exp Nephrol. 2017;21(3):383-390. https://doi.org/10.1007/s10157-016-1296-2
  60. Gharaibeh KA, Hamadah AM, El-Zoghby ZM, et al. Cystatin C Predicts Renal Recovery Earlier Than Creatinine Among Patients With Acute Kidney Injury. Kidney Int Rep. 2017;3(2):337-342. https://doi.org/10.1016/j.ekir.2017.10.012
  61. Peralta CA, Shlipak MG, Judd S, et al. Detection of chronic kidney disease with creatinine, cystatin C, and urine albumin-to-creatinine ratio and association with progression to end-stage renal disease and mortality. JAMA. 2011;305(15):1545-52. https://doi.org/10.1001/jama.2011.468
  62. Bevc S, Hojs N, Knehtl M, Ekart R, Hojs R. Cystatin C as a predictor of mortality in elderly patients with chronic kidney disease. Aging Male. 2019;22(1):62-67. https://doi.org/10.1080/13685538.2018.1479386
  63. Shin MJ, Song SH, Kwak IS, et al. Serum cystatin C as a predictor for cardiovascular events in end-stage renal disease patients at the initiation of dialysis. Clin Exp Nephrol. 2012;16(3):456-63. https://doi.org/10.1007/s10157-011-0583-1
  64. Shlipak MG, Matsushita K, Ärnlöv J, et al; CKD Prognosis Consortium. Cystatin C versus creatinine in determining risk based on kidney function. N Engl J Med. 2013;369(10):932-43. https://doi.org/10.1056/NEJMoa1214234
  65. Lees JS, Welsh CE, Celis-Morales CA, et al. Glomerular filtration rate by differing measures, albuminuria and prediction of cardiovascular disease, mortality and end-stage kidney disease. Nat Med. 2019;25(11):1753-1760. Epub 2019 Nov 7. Erratum in: Nat Med. 2020;26(8):1308. https://doi.org/10.1038/s41591-020-0996-z
  66. Benoit SW, Ciccia EA, Devarajan P. Cystatin C as a biomarker of chronic kidney disease: latest developments. Expert Rev Mol Diagn. 2020;20(10):1019-1026. https://doi.org/10.1080/14737159.2020.1768849
  67. Stevens LA, Schmid CH, Greene T, et al. Factors other than glomerular filtration rate affect serum cystatin C levels. Kidney Int. 2009;75(6):652-60. https://doi.org/10.1038/ki.2008.638
  68. Qin Z, Li H, Jiao P, et al. The value of urinary interleukin-18 in predicting acute kidney injury: a systematic review and meta-analysis. Ren Fail. 2022;44(1):1717-1731. https://doi.org/10.1080/0886022X.2022.2133728
  69. Lin X, Yuan J, Zhao Y, Zha Y. Urine interleukin-18 in prediction of acute kidney injury: a systemic review and meta-analysis. J Nephrol. 2015;28(1):7-16. https://doi.org/10.1007/s40620-014-0113-9
  70. Radwan GAE, Yousef AE, Bayomy MF. Serum interleukin 18 level in kidney diseases and age. Urol Ann. 2024;16(2):133-139. https://doi.org/10.4103/ua.ua_140_22
  71. Luan J, Fu J, Jiao C, et al. IL-18 deficiency ameliorates the progression from AKI to CKD. Cell Death Dis. 2022;13(11):957. Published 2022 Nov 15. https://doi.org/10.1038/s41419-022-05394-4
  72. Yaribeygi H, Atkin SL, Sahebkar A. Interleukin-18 and diabetic nephropathy: A review. J Cell Physiol. 2019;234(5):5674-5682. https://doi.org/10.1002/jcp.27427
  73. Fujita T, Ogihara N, Kamura Y, et al. Interleukin-18 contributes more closely to the progression of diabetic nephropathy than other diabetic complications. Acta Diabetol. 2012;49(2):111-117. https://doi.org/10.1007/s00592-010-0178-4
  74. Bagshaw SM, Bellomo R, Devarajan P, et al. Review article: Acute kidney injury in critical illness. Can J Anaesth. 2010;57(11):985-998. https://doi.org/10.1007/s12630-010-9375-4
  75. Nguyen MT, Devarajan P. Biomarkers for the early detection of acute kidney injury. Pediatr Nephrol. 2008;23(12):2151-2157. https://doi.org/10.1007/s00467-007-0470-x
  76. Noorani A, Sadat U, Rollins KE, et al. Assessment of Renal Injury in Patients Undergoing Elective EVAR Using Urinary Neutrophil Gelatin-Associated Lipocalin, Interleukin 18, and Retinol-Binding Protein. 2017;68(6):547-552. https://doi.org/10.1177/0003319716672524
  77. A Al-Saegh RM, Mohanad MA, Khudhair NJ, R Al-Mukhtar MA. Using urinary Interleukin-18 as a potential marker for early detection of acute kidney injury in intensive care unit. Saudi J Kidney Dis Transpl. 2021;32(2):341-347. https://doi.org/10.4103/1319-2442.335445
  78. Shaker AM, Mohamed MF, Thabet KK, Ramzy T, Abdelhamid YM. Serum Interleukin-18, Kidney Injury Molecule-1, and the Renal Resistive Index for Predicating Acute Kidney Injury in Critically Ill Patients with Sepsis. Saudi J Kidney Dis Transpl. 2023;34(Suppl 1):S153-S160. https://doi.org/10.4103/sjkdt.sjkdt_56_22
  79. Nisula S, Yang R, Poukkanen M, et al. Predictive value of urine interleukin-18 in the evolution and outcome of acute kidney injury in critically ill adult patients. Br J Anaesth. 2015;114(3):460-468. https://doi.org/10.1093/bja/aeu382
  80. Lin CY, Chang CH, Fan PC, et al. Serum interleukin-18 at commencement of renal replacement therapy predicts short-term prognosis in critically ill patients with acute kidney injury. PLoS One. 2013;8(5):e66028. Published 2013 May 31. https://doi.org/10.1371/journal.pone.0066028
  81. Liu Y, Guo W, Zhang J, et al. Urinary interleukin 18 for detection of acute kidney injury: a meta-analysis. Am J Kidney Dis. 2013;62(6):1058-1067. https://doi.org/10.1053/j.ajkd.2013.05.014
  82. Coca SG, Nadkarni GN, Garg AX, et al. First Post-Operative Urinary Kidney Injury Biomarkers and Association with the Duration of AKI in the TRIBE-AKI Cohort. PLoS One. 2016;11(8):e0161098. Published 2016 Aug 18. https://doi.org/10.1371/journal.pone.0161098
  83. Mutlu H, Gündüz E, Titiz TA, Küçükçetin İÖ. Investigation of AKI with Early Biomarkers After Cardiac Surgery. Braz J Cardiovasc Surg. 2020;35(5):722-731. Published 2020 Oct 1. https://doi.org/10.21470/1678-9741-2019-0178
  84. Devarajan P. Biomarkers for the early detection of acute kidney injury. Curr Opin Pediatr. 2011;23(2):194-200. https://doi.org/10.1097/MOP.0b013e328343f4dd
  85. Gonzalez F, Vincent F. Biomarkers for acute kidney injury in critically ill patients. Minerva Anestesiol. 2012 Dec;78(12):1394-403. Epub 2012 Oct 2. PMID: 23032924. https://pubmed.ncbi.nlm.nih.gov/23032924/
  86. Puthumana J, Ariza X, Belcher JM, Graupera I, Ginès P, Parikh CR. Urine Interleukin 18 and Lipocalin 2 Are Biomarkers of Acute Tubular Necrosis in Patients With Cirrhosis: A Systematic Review and Meta-analysis. Clin Gastroenterol Hepatol. 2017;15(7):1003-1013.e3. https://doi.org/10.1016/j.cgh.2016.11.035
  87. Sirota JC, Walcher A, Faubel S, et al. Urine IL-18, NGAL, IL-8 and serum IL-8 are biomarkers of acute kidney injury following liver transplantation. BMC Nephrol. 2013;14:17. Published 2013 Jan 17. https://doi.org/10.1186/1471-2369-14-17
  88. Sung WC, Yu HP, Tsai YF, Chung PC, Lin CC, Lee WC. The ratio of plasma interleukin-18 is a sensitive biomarker for acute kidney injury after liver transplantation. Transplant Proc. 2014;46(3):816-817. https://doi.org/10.1016/j.transproceed.2013.09.055
  89. Kuo P-Y, Tsai K-F, Wu P-J, et al. Interleukin-18 and Gelsolin Are Associated with Acute Kidney Disease after Cardiac Catheterization. Biomolecules. 2023; 13(3):487. https://doi.org/10.3390/biom13030487
  90. Ren H, Zhou X, Dai D, et al. Assessment of urinary kidney injury molecule-1 and interleukin-18 in the early post-burn period to predict acute kidney injury for various degrees of burn injury. BMC Nephrol. 2015;16:142. Published 2015 Aug 18. https://doi.org/10.1186/s12882-015-0140-3
  91. Choudhary A, Basu S, Dey SK, et al. Association and prognostic value of serum Cystatin C, IL-18 and Uric acid in urological patients with acute kidney injury. Clin Chim Acta. 2018;482:144-148. https://doi.org/10.1016/j.cca.2018.04.005
  92. Urbschat A, Obermüller N, Haferkamp A. Biomarkers of kidney injury. Biomarkers. 2011;16 Suppl 1:S22-S30. https://doi.org/10.3109/1354750X.2011.587129
  93. Duff S, Irwin R, Cote JM, et al. Urinary biomarkers predict progression and adverse outcomes of acute kidney injury in critical illness. Nephrol Dial Transplant. 2022;37(9):1668-1678. https://doi.org/10.1093/ndt/gfab263
  94. Yin C, Wang N. Kidney injury molecule-1 in kidney disease. Ren Fail. 2016;38(10):1567-1573. https://doi.org/10.1080/0886022X.2016.1193816
  95. Tanase DM, Gosav EM, Radu S, et al. The Predictive Role of the Biomarker Kidney Molecule-1 (KIM-1) in Acute Kidney Injury (AKI) Cisplatin-Induced Nephrotoxicity. Int J Mol Sci. 2019;20(20):5238. Published 2019 Oct 22. https://doi.org/10.3390/ijms20205238
  96. Anandkumar DG, Dheerendra PC, Vellakampadi D, Ramanathan G. Kidney injury molecule-1; is it a predictive marker for renal diseases? J Nephropharmacol. 2023 Mar 18;12(2):e10572. https://doi.org/10.34172/npj.2023.10572
  97. Lin Q, Chen Y, Lv J, et al. Kidney injury molecule-1 expression in IgA nephropathy and its correlation with hypoxia and tubulointerstitial inflammation. Am J Physiol Renal Physiol. 2014;306(8):F885-F895. https://doi.org/10.1152/ajprenal.00331.2013
  98. Moresco RN, Bochi GV, Stein CS, et al. Urinary kidney injury molecule-1 in renal disease. Clin Chim Acta. 2018;487:15-21. https://doi.org/10.1016/j.cca.2018.09.011
  99. Zhang Z, Cai CX. Kidney injury molecule-1 (KIM-1) mediates renal epithelial cell repair via ERK MAPK signaling pathway. Mol Cell Biochem. 2016;416(1-2):109-116. https://doi.org/10.1007/s11010-016-2700-7
  100. Ichimura T, Hung CC, Yang SA, Stevens JL, Bonventre JV. Kidney injury molecule-1: a tissue and urinary biomarker for nephrotoxicant-induced renal injury. Am J Physiol Renal Physiol. 2004;286(3):F552-F563. https://doi.org/10.1152/ajprenal.00285.2002
  101. Shao X, Tian L, Xu W, et al. Diagnostic value of urinary kidney injury molecule 1 for acute kidney injury: a meta-analysis. PLoS One. 2014;9(1):e84131. Published 2014 Jan 3. https://doi.org/10.1371/journal.pone.0084131
  102. Zhang B, Song Y, Ma Q, Yang J, Bai L. Expression and Significance of KIM-1, NGAL, and HO-1 in Patients with Acute Kidney Injury After Cardiac Valve Replacement. J Inflamm Res. 2023;16:2755-2761. Published 2023 Jun 30. https://doi.org/10.2147/JIR.S410338
  103. Medić B, Rovčanin B, Basta Jovanović G, Radojević-Škodrić S, Prostran M. Kidney Injury Molecule-1 and Cardiovascular Diseases: From Basic Science to Clinical Practice. Biomed Res Int. 2015;2015:854070. https://doi.org/10.1155/2015/854070
  104. Torregrosa I, Montoliu C, Urios A, et al. Urinary KIM-1, NGAL and L-FABP for the diagnosis of AKI in patients with acute coronary syndrome or heart failure undergoing coronary angiography. Heart Vessels. 2015;30(6):703-711. https://doi.org/10.1007/s00380-014-0538-z
  105. Yin W, Kumar T, Lai Z, et al. Kidney injury molecule-1, a sensitive and specific marker for identifying acute proximal tubular injury, can be used to predict renal functional recovery in native renal biopsies. Int Urol Nephrol. 2019;51(12):2255-2265. https://doi.org/10.1007/s11255-019-02311-1
  106. Tu Y, Wang H, Sun R, et al. Urinary netrin-1 and KIM-1 as early biomarkers for septic acute kidney injury. Ren Fail. 2014;36(10):1559-1563. https://doi.org/10.3109/0886022X.2014.949764
  107. Satirapoj B, Pooluea P, Nata N, Supasyndh O. Urinary biomarkers of tubular injury to predict renal progression and end stage renal disease in type 2 diabetes mellitus with advanced nephropathy: A prospective cohort study. J Diabetes Complications. 2019;33(9):675-681. https://doi.org/10.1016/j.jdiacomp.2019.05.013
  108. Hwang S, Park J, Kim J, et al. Tissue expression of tubular injury markers is associated with renal function decline in diabetic nephropathy. J Diabetes Complications. 2017;31(12):1704-1709. https://doi.org/10.1016/j.jdiacomp.2017.08.009
  109. Huisman GJJ, Spraakman NA, Koomen JV, et al. Urinary Biomarkers in a Living Donor Kidney Transplantation Cohort-Predictive Value on Graft Function. Int J Mol Sci. 2023;24(6):5649. Published 2023 Mar 15. https://doi.org/10.3390/ijms24065649
  110. Sun Z, Gao Z, Li X, Zheng X, Wang W, Qiao P. Perfusate Neutrophil Gelatinase-Associated Lipocalin, Kidney Injury Molecular-1, Liver-Type Fatty Acid Binding Protein, and Interleukin-18 as Potential Biomarkers to Predict Delayed Graft Function and Long-Term Prognosis in Kidney Transplant Recipients: A Single-Center Retrospective Study. Med Sci Monit. 2023;29:e938758. Published 2023 Mar 4. https://doi.org/10.12659/MSM.938758
  111. Zhang PL, Rothblum LI, Han WK, Blasick TM, Potdar S, Bonventre JV. Kidney injury molecule-1 expression in transplant biopsies is a sensitive measure of cell injury. Kidney Int. 2008;73(5):608-614. https://doi.org/10.1038/sj.ki.5002697
  112. Bank JR, van der Pol P, Vreeken D, et al. Kidney injury molecule-1 staining in renal allograft biopsies 10 days after transplantation is inversely correlated with functioning proximal tubular epithelial cells. Nephrol Dial Transplant. 2017;32(12):2132-2141. https://doi.org/10.1093/ndt/gfx286
  113. Brilland B, Boud’hors C, Wacrenier S, et al. Kidney injury molecule 1 (KIM-1): a potential biomarker of acute kidney injury and tubulointerstitial injury in patients with ANCA-glomerulonephritis. Clin Kidney J. 2023;16(9):1521-1533. Published 2023 Apr 3. https://doi.org/10.1093/ckj/sfad071
  114. Ostermann M, Legrand M, Meersch M, Srisawat N, Zarbock A, Kellum JA. Biomarkers in acute kidney injury. Ann Intensive Care. 2024;14(1):145. Published 2024 Sep 15. https://doi.org/10.1186/s13613-024-01360-9
  115. Samoni S, De Rosa S, Ronco C, Castellano G. Update on persistent acute kidney injury in critical illnesses. Clin Kidney J. 2023;16(11):1813-1823. Published 2023 May 11. https://doi.org/10.1093/ckj/sfad107
  116. Klementa V, Petejova N, Zadrazil J, et al. Prediction of Acute Kidney Injury Development in Critically Ill Septic Patients Based on NGAL Determination. Physiol Res. 2024;73(6):1001-1011. https://doi.org/10.33549/physiolres.935336
  117. Strader M, Imran S, Tariq A, et al. Clinical Implementation of Urinary NGAL Testing for Diagnosing Acute Kidney Injury in an Academic Tertiary Care Medical Centre. Kidney360. Published online August 13, 2025. https://doi.org/10.34067/KID.0000000887
  118. Honore PM, Jacobs R, Joannes-Boyau O, et al. Biomarkers for early diagnosis of AKI in the ICU: ready for prime time use at the bedside? Ann Intensive Care. 2012;2(1):24. Published 2012 Jul 2. https://doi.org/10.1186/2110-5820-2-24
  119. Zarbock A, Kellum JA, Schmidt C, et al. Effect of Early vs Delayed Initiation of Renal Replacement Therapy on Mortality in Critically Ill Patients With Acute Kidney Injury: The ELAIN Randomized Clinical Trial. JAMA. 2016;315(20):2190-2199. https://doi.org/10.1001/jama.2016.5828
  120. Wald R, Kirkham B, daCosta BR, et al. Fluid balance and renal replacement therapy initiation strategy: a secondary analysis of the STARRT-AKI trial. Crit Care. 2022;26(1):360. Published 2022 Nov 24. https://doi.org/10.1186/s13054-022-04229-0

Come valutare la velocità di filtrazione glomerulare e quale metodo è considerato il più affidabile?

Abstract

La prevalenza della malattia renale cronica (CKD) continua ad aumentare a livello globale, sia per un aumento della morbilità e della mortalità associate sia per le implicazioni significative sulla qualità della vita dei pazienti e sulle economie nazionali. La malattia renale cronica spesso progredisce senza essere riconosciuta dai pazienti e dai sanitari, nonostante la diagnosi si basi su due semplici misure di laboratorio: velocità di filtrazione glomerulare stimata (eGFR) e analisi delle urine. La misura della GFR è correlata alla fisiologia renale, in particolare al concetto di clearance, con la creatinina identificata come un marcatore endogeno adatto per stimare la clearance della creatinina (CrCl). In base a questo principio sono state sviluppate varie equazioni per calcolare la CrCl o il GFR stimato (eGFR) utilizzando quattro variabili che incorporano la creatinina e alcune informazioni demografiche, come sesso ed età. Tuttavia, la misurazione della creatinina richiede la standardizzazione per ridurre al minimo la variabilità del dosaggio tra i laboratori. L’accuratezza di queste equazioni rimane controversa in alcuni sottogruppi di pazienti. Per questi motivi, sono stati ideati ulteriori modelli matematici per migliorare la stima della CrCl, in particolare quando la raccolta delle urine non è praticabile, in pazienti anziani o debilitati e in soggetti con traumi, diabete o obesità. Attualmente, l’eGFR negli adulti può essere immediatamente misurato e riportato utilizzando equazioni basate sulla creatinina tracciabili tramite spettrometria di massa con diluizione isotopica. In conclusione, sfruttando le conoscenze della fisiologia renale, l’eGFR può essere impiegato clinicamente per la diagnosi precoce e il trattamento della malattia renale cronica, nonché come strumento di sanità pubblica per stimarne la prevalenza.

Parole chiave: marcatori di filtrazione glomerulare, creatinine, cistatina C, inulina, iohexol

Ci spiace, ma questo articolo è disponibile soltanto in inglese.

Introduction

The prevalence of chronic kidney disease (CKD) continues to escalate globally, accompanied by an increase in morbidity, mortality, and significant implications for the quality of life of patients and the economies of nations. Any clinical condition resulting from a reduction in the number of functioning nephrons can progress into chronic renal failure, defined by the KDIGO guidelines as “abnormalities in kidney structure or function, present for 3 months, with health implications” [1].

In the real world, chronic kidney disease is a silent ailment often progressing unnoticed by patients and physicians, although the diagnosis relies on two simple laboratory measures: estimated GFR (eGFR) and urine analysis (screening for albuminuria/proteinuria). The glomerular filtration rate remains the premier comprehensive indicator of renal function as it assesses renal clearance and is directly related to the functioning renal mass, serving to classify CKD into stages, calculate medication dosages, and prepare for invasive studies with contrast medium. Early diagnosis of chronic kidney disease aids in delaying progression and reducing associated morbidity and mortality.

 

Identification of the Glomerular Filtration Process for GFR Measurement in Clinical Practice

Carl Ludwig (1816-1895), pioneered of glomerular filtration identified the glomerulus as a filter. This filtration is regulated by the hydrostatic pressure and modulated by the contraction and vasodilation of the afferent and efferent arterioles. He further hypothesized that the filtered volume decreased along the tubules due to reabsorption, thereby concentrating the end products in the urine [2]. However, to apply the concept of GFR in clinical settings, it was imperative to identify a solute removed solely by filtration, without reabsorption or secretion in the nephron. Later Paul Rehberg pinpointed creatinine as such a solute, given its endogenous production, filtration, and presumed lack of reabsorption or excretion.

Comparative summary of GFR estimation equations
Figure 1. Comparative summary of GFR estimation equations, including Cockcroft-Gault, simplified MDRD-4, CKD-EPI creatinine and cystatin C, and the FAS method. These formulas incorporate variables such as age, weight, serum creatinine, and patient demographics to determine renal function.

 

Estimation of GFR with Endogenous Markers

Creatinine-Based Glomerular Filtration Estimation

Creatinine remains the most widely utilized endogenous marker for estimating renal function in clinical practice, research, and animal models. It is a waste product of regular muscle metabolism. Creatinine, not being protein-bound, is freely filtered by the glomeruli; however, its synthesis is not constant, as it is determined by daily protein intake and muscle trophism. It is also subject to both secretory and reabsorptive mechanisms [3]. These conditions restrict the utility of creatinine as a renal function marker. Gender differences in tubular secretion have also been documented: males may secrete more creatinine than females, which could result in discrepancies in GFR estimation between male and female animals [4].

The initial method to measure creatinine, developed in 1886, was the alkaline picric acid reaction of Jaffé (a colorimetric method). This method’s interference with chromogens, such as bilirubin, glucose, or hemoglobin, led to inaccuracies in humans. In rodents, non-specific chromogens could overestimate creatinine by a factor of five. Different methods have been adapted to measure serum creatinine. The enzymatic determination, now considered the reference method in rodents, was validated in 2007 with various reactions with the aid of creatininase, creatinase, and sarcosine oxidase [5]. The measurement of creatinine in serum is prone to different types of error, interferences and imprecision. Serum creatinine certainly represents the most practical and least expensive measurement for stable glomerular filtration rate, however it presents some limitations in the interpretation of the results which may be secondary to both tubular secretion and the presence of muscle mass and protein intake. Even the absolute value of creatinine is subject to some variations such as the reference intervals of each analysis method of each laboratory with the risk of altering each glomerular filtration rate analysis equation. There are limitations in estimating creatinine secondary to muscle trophism because it is a product of muscle catabolism and results difficult in patients with extremely low or high muscular mass (e.g., anorexia, obesity or weight lifter). Creatinine is secreted by tubules and this explains why creatinine cleareance overestimates true GFR. Drugs, such as trimethoprim and cimetidine, also interfere with this tubular secretion and this explains why during their intake there is an increase in creatinine values ​​without evident alterations in GFR. The absolute value of creatinine could be altered in some pathological conditions such as liver failure and rhabdomyolysis. The absolute value of creatinine has physiological limits for an accurate estimate of the glomerular filtration rate [20].

Creatinine Clearance Over 24 Hours and Estimation of GFR Using Endogenous and Exogenous Markers

24-hour creatinine clearance has been a prevalent method for assessing GFR in animal models. Yet, it is crucial to acknowledge that the limitations of serum creatinine as a renal function marker impact the precision and accuracy of the 24-hour collection [6]. Blood samples are necessary to measure serum creatinine.

GFR Estimation Using Cystatin-C

Cystatin-C (CysC) is a low molecular weight protein (13KDa) of the family of cysteine ​​protease inhibitors. It is produced by all the nucleated cells of the body, filtered by the glomerulus, and then reabsorbed and metabolised by tubular epithelial cells, excluding its use for clearance on 24 hour urine. Like cratinine, the determination of cystatin C is influenced by factors such as sex, age and chronic inflammatory state [7], but it provides a more precise estimate of glomerular filtration as it is not affected by variables such as muscle mass and activity, or dietary protein intake.

 

GFR Estimation with Exogenous Markers: Inulin Clearance

The fructose polymer inulin has always represented a specific method for medical students for measuring glomerular filtration [8] due to the intrinsic characteristics of the molecule; in fact inulin is not metabolised, does not bind to plasma proteins and is freely filtered by the glomeruli without being reabsorbed or secreted by the tubular cells. However, considering inulin as the gold standard of the glomerular filtration method presents some limitations: the high cost and cumbersome methods for developing the process such as use with radioactive markers, poor solubility in water and demanding preparation for the solution to be injected (substance dissolve, filter and heat at high temperatures for many hours to remove inulin fragments). Once prepared, inulin is administered as a single intravenous bolus or continuous infusion and plasma and/or urine are collected at different times to calculate clearance. All these steps do not make this method universal.

 

Sinistrin: The New Inulin?

The measurement of GFR can also be obtained by evaluating the kinetics of Sinistrin FITC and in particular by estimating the half-life. Sinistrin has the advantage of having a lower molecular weight (3500 Da) compared to inulin, it is hightly soluble in aqueous solvents at room temperature, it can be used and labeled with FITC fluorescein [9]. Unlike inulin, it does not require any filtration and has the advantage of being able to be used using transcutaneous devices. An instrument composed of two LEDs is required for measuring fluorescence and transcutaneous GFR. The method consists in the intravenous infusion of Sinistrin with the FITC chromophore which emits the fluorescence captured by the instrument. Transcutaneous measurement has proven to be a good method for measuring renal function in murine models and has the advantage, especially in animals, of measuring glomerular filtration in the absence of particular traumas [21].

 

Transcutaneous Methods for GFR Measurement

To determine glomerular filtration, the intravenous injection of a sinister FITC molecule was studied and then the variation in fluorescence was studied using a device positioned on the skin. The change in fluorescence is used to calculate the elimination half-life of the marker and then convert the half-life data to GFR (ml/min). The main advantage of this method is its non-invasiveness, however it has limitations as it is an indirect method for measuring GFR and therefore requires conversion factors. The main advantage is its independence from blood/urine sampling and laboratory tests with real-time GFR examination, however a limitation to be evaluated is the high cost of the device ($1000) which makes it impractical for clinical practice [10].

 

Radiolabeled Tracers

The two most commonly used radiolabeled markers are ethylenediaminetetraacetic acid with radioactive chromium-51 (51Cr-EDTA) and diethylenetriamine pentaacetic acid with radioactive technetium-99 (99mTc-DTPA), both of which are low molecular weight and freely filtered by the glomerulus.

The method consists in measuring the plasma and urinary clearance of single intravenous injections of radiolabeled substances or alternatively intraperitoneal injection [11]. Blood and urine samples are taken and processed using a gamma counter that estimates GFR. 99mTc-DTPA has been used in healthy male Wistar rats and in animals with chronic kidney disease or doxorubicin-induced nephritic syndrome [12]. The main limitation of this technique derives from the use of radioisotopes, which are not easy to find and which require special authorization and specific conservation; furthermore it presents toxicity for operators who must use specific precautions and careful waste management.

99mTc can dissociate from DTPA and up to 13% of 99mTc-DTPA can bind to plasma proteins, resulting in an underestimate of GFR [13]. These markers could be useful for verifying GFR but are not preferable in clinical practice.

 

Non-Radiolabeled Contrast Agents in GFR Assessment

Among the various possibilities for measuring GFR is iothalamate, an ionic contrast agent derived from tri-iodobenzoic acid with a molecular weight of 637 Kda. Bell proposed a rapid HPLC method to detect iothalamate and para-aminohippuric acid in rat serum and urine [14], giving an estimate of both GFR and renal blood flow. This method is not easy to apply as it involves both central venous catheterization, a method not without serious side effects, and the simultaneous collection of blood and urine.

 

Iohexol/Iohexol-DBS

Iohexol (Omnipaque™, GE Healthcare) is a molecule used as a contrast agent. It is excreted unmetabolised by glomerular filtration, without reabsorption or secretion by renal tubular cells without undergoing hepatic metabolism or interference with blood cells. Its use as a reference method for measuring GFR was established almost 30 years ago in humans [15]. In recent years, the filtration of iohexol in mice has been studied by intravenous injection and subsequent blood sampling for pharmacokinetic analysis. Iohexol is measured by HPLC chromatographic analysis. Schultz et al. described the plasma clearance of iohexol in rats in 2014 using liquid chromatography-electrospray-mass spectrometry (LC-ESI-MS). They administered different doses of iohexol via the tail vein to male HsdRCCHan:WIS rats, and the animals were sacrificed at different times after infection with iohexol (15, 30, 60, and 90 minutes) to obtain blood samples. Passos et al. validated the plasma clearance of iohexol in rats [16] against the “classical” gold standard, inulin clearance, using capillary electrophoresis, observing a correlation between iohexol and inulin clearance (r = 0.792). However, the procedure required large amounts of blood. Carrara proposed the measurement of GFR through experiments on mice using the following scheme: administration of iohexol (129.4 mg) intravenously and subsequent determination on four blood samples after the infusion at times (20, 40, 120, 140 minutes) [17]. While Luis-Lima proposed a further simplified scheme with fewer side effects, always in mice; intravenous administration of 6.47 mg of iohexol and subsequent blood sampling (approximately 10 μL each) after the infusion at times (15, 35, 55 and 75 minutes) with determination of iohexol by HPLC-UV on the blood and with factor correction equal to 0.89. The advantage of both methods was represented by the fact that they were comparable in their results not only in mice with normal renal function but also in mice with CKD and with a single kidney following nephrectomy [16].

This method has the advantage of using a small quantity of blood, approximately 10 μL, offering the advantage of carrying out serial samples over time to evaluate the progress of renal function.

Rodríguez-Rodríguez AE et al. have proposed the possibility of using dried blood samples (DBS) while maintaining adequate precision in sample processing [16]. The method consisted of sampling 5 μL of blood with heparin tubes at times 15, 30, 45, 60, 75 minutes after the infusion of Iohexol and subsequent drying of the blood sample on filter paper (Whatman 903, GE Healthcare) to 24 hours and subsequent extraction with 5% perchloric acid with centrifuge [18]. The measurement of Iohexol was carried out with the HPLC method; this procedure showed high precision in the determination of GFR in mice.

Turner established a new method of determining GFR using Iohexol with two blood samples and compared it to better known methods such as inulin, creatinine and cystatin-C [19]. Intravenous infusion of 25 mg/kg of Iohexol was performed and blood samples were taken at times 2, 5, 10, 20, 30, 60, 90, 120, 180, 240, 300 minutes; the result shawed that the samples taken at the 30 and 90 minute periods represented the average of the values ​​of all eleven blood samples. Thus, Iohexol was proposed as a method to determine GFR through a single intravenous infusion of 25 mg/kg of Iohexol, with subsequent measurements taken within 30 and 90 minutes.

Iohexol represents a precise method for measuring GFR however it may have measurement errors due to sample preparation.

 

Conclusions

The study of the various methods for calculating GFR is still a topic of study today so that we can achieve a simple, rapid and reproducible measurement in every peripheral structure. The ideal method should avoid 24-hour urine collection, reduce the amount of blood, avoid radiolabeled substances and speed in sample calculation. We have listed several types, each with potential disadvantages. Creatinine and cystatin-C, despite being widely used, sometimes have limitations in determining the real GFR. Radiolabeled markers (99mTc-DTPA and 51Cr-EDTA) are cheap but unsafe and should be replaced with an alternative method. Inulin represents the most precise method but is difficult to reproduce in a clinical environment due to the costs and complexity of the procedure. Iothalamate is less precise than inulin but more convenient and easier to use. Iohexol represents a precise and safe method but to date it has been studied in mouse models. An alternative may be represented by fluorescent markers such as FITC inulin or FITC sinistrin, also used in the transcutaneous method with the advantage of instantaneous measurement and no use of optimal methodical blood sampling in animals [6]. In conclusion, the method for measuring GFR should depend on the care setting, the resources available, the experience of the researcher and the safety and well-being of the animals.

 

Bibliography

  1. Awan AAY, Berenguer MC, Bruchfeld A, Fabrizi F, et al. Prevention, Diagnosis, Evaluation, and Treatment of Hepatitis C in Chronic Kidney Disease: Synopsis of the Kidney Disease: Improving Global Outcomes 2022 Clinical Practice Guideline. Ann Intern Med. 2023 Dec;176(12):1648-1655. https://doi.org/10.7326/M23-2391.
  2. Inker LA, Silvia Titan. Measurement and Estimation of GFR for Use in Clinical Practice: Core Curriculum 2021. Am J Kidney Dis. 2021. https://doi.org/10.1053/j.ajkd.2021.04.016.
  3. Yan AF, Williams MY, Shi Z, Oyekan R, Yoon C, Bowen R, Chertow GM. Bias and Accuracy of Glomerular Filtration Rate Estimating Equations in the US: A Systematic Review and Meta-Analysis. JAMA Netw Open. 2024 Mar 4;7(3):e241127. https://doi.org/10.11001/jamanetworkopen.2024.1127.
  4. Jing J, Pattaro C, Hoppmann A, Okada Y; CKDGen Consortium; Fox CS, Köttgen A. Combination of mouse models and genomewide association studies highlights novel genes associated with human kidney function. Kidney Int. 2016 Oct;90(4):764-73. https://doi.org/10.1016/j.kint.2016.04.004.
  5. Mousa MA, Asman AS, Ali RMJ, Sayed RKA, Majrashi KA, Fakiha KG, Alhotan RA, Selim S. Impacts of Dietary Lysine and Crude Protein on Performance, Hepatic and Renal Functions, Biochemical Parameters, and Histomorphology of Small Intestine, Liver, and Kidney in Broiler Chickens. Vet Sci. 2023 Jan 29;10(2):98. https://doi.org/10.3390/vetsci10020098.
  6. Teixido-Trujillo S, Luis-Lima S, López-Martínez M, et al. Measured GFR in murine animal models: review on methods, techniques, and procedures. Pflugers Arch. 2023 Nov;475(11):1241-1250. https://doi.org/10.1007/s00424-023-02841-9.
  7. Ntaios G, Brederecke J, Ojeda FM, Zeller T, Blankenberg S, Schnabel RB. New race-free creatinine- and cystatin C-based equations for the estimation of glomerular filtration rate and association with cardiovascular mortality in the AtheroGene study. Intern Emerg Med. 2024 Feb 13. https://doi.org/10.1007/s11739-023-03529-9.
  8. Besseling PJ, Pieters TT, Nguyen ITN, de Bree PM, Willekes N, Dijk AH, et al. A plasma creatinine- and urea-based equation to estimate glomerular filtration rate in rats. Am J Physiol Renal Physiol. 2021 Mar 1;320(3):F518-F524. https://doi.org/10.1152/ajprenal.00656.2020.
  9. Chan G, Pino CJ, Johnston KA, Humes HD. Estimating Changes in Glomerular Filtration Rate With Fluorescein Isothiocyanate-Sinistrin During Renal Replacement Therapy. ASAIO J. 2023 Aug 1;69(8):810-815. https://doi.org/10.1097/MAT.0000000000001947.
  10. Hauser-Kawaguchi A, Milne M, Li F, Lee TY, Luyt LG. The development of a near infrared inulin optical probe for measuring glomerular filtration rate. Int J Biol Macromol. 2019 Feb 15;123:255-260. https://doi.org/10.1016/j.ijbiomac.2018.11.034.
  11. Balouzet C, Michon-Colin A, Dupont L, Vidal-Petiot E, Prot-Bertoye C, et al. Comparison of (99m)Tc-DTPA and (51)Cr-EDTA for glomerular filtration rate measurement with the continuous infusion method. J Nephrol. 2023 Dec;36(9):2457-2465. https://doi.org/10.1007/s40620-023-01612-0.
  12. Iversen E, Bengaard AK, Leegaard Andersen A, Tavenier J, et al. Performance of Panel-Estimated GFR Among Hospitalized Older Adults. Am J Kidney Dis. 2023 Dec;82(6):715-724. https://doi.org/10.1053/j.ajkd.2023.05.004.
  13. Lee HT, Jan M, Bae SC, Joo JD, Goubaeva FR, Yang J, Kim M. A1 adenosine receptor knockout mice are protected against acute radiocontrast nephropathy in vivo. 2006. Am J Physiol Renal Physiol 290:F1367–F1375. https://doi.org/10.1152/ajprenal.00347.2005.
  14. Pottel H, Cavalier E, Björk J, Nyman U, Grubb A, Ebert N, et al. Standardization of serum creatinine is essential for accurate use of unbiased estimated GFR equations: evidence from three cohorts matched on renal function.  Clin Kidney J. 2022 Aug 3;15(12):2258-2265. https://doi.org/10.1093/ckj/sfac182.
  15. Delanaye P, Pottel H, Cavalier E, Flamant M, Stehlé T, Mariat C. Diagnostic standard: assessing glomerular filtration rate. Nephrol Dial Transplant. 2023 Nov 9:gfad241. https://doi.org/10.1093/ndt/gfad241.
  16. Rodríguez-Rodríguez AE, Luis-Lima S, Donate-Correa J, Diaz-Martín L, Arnau MR, Jiménez-Sosa A, Gaspari F, Ortiz A, Porrini E. Iohexol plasma clearance simplified by Dried Blood Spot (DBS) sampling to measure renal function in conscious mice. Sci Rep. 2021 Feb 25;11(1):4591. https://doi.org/10.1038/s41598-021-83934-2.
  17. Fabiola Carrara, Nadia Azzollini, Giovanni Nattino, et al. Simplified Method to Measure Glomerular Filtration Rate by Iohexol Plasma Clearance in Conscious Rats. 2016. 133(1):62-70. https://doi.org/10.1159/000445843.
  18. Dejaco A, Dorn C, Paal M, Gruber M, Graf BM, Kees MG. Determination of glomerular filtration rate “en passant” after high doses of iohexol for computed tomography in intensive care medicine-a proof of concept. Front Pharmacol. 2024 Feb 1;15:1346343. https://doi.org/10.3389/fphar.2024.1346343.
  19. Turner ME, Laverty KJ, Jeronimo PS, Kaufmann M, Jones G, White CA, Holden RM, Adams MA. Validation of a routine two-sample iohexol plasma clearance assessment of GFR and an evaluation of common endogenous markers in a rat model of CKD. Physiol Rep. 2017 May;5(9):e13205. https://doi.org/10.14814/phy2.13205.
  20. Delanaye P, Cavalier E, Pottel H. Serum Creatinine: Not So Simple! Nephron. 2017;136(4):302-308. https://doi.org/10.1159/000469669.
  21. Schreiber A, Shulhevich Y, Geraci S, Hesser J, Stsepankou D, et al. Transcutaneous measurement of renal function in conscious mice. Am J Physiol Renal Physiol. 2012 Sep;303(5):F783-8. https://doi.org/10.1152/ajprenal.00279.2012.