What are the most effective real-time markers of kidney cell damage?

NGAL, [TIMP‑2]·[IGFBP7], and KIM-1 are the most effective real-time markers of kidney cell damage, showing high diagnostic accuracy when measured at appropriate time windows and achieving superior performance when combined in multi-marker panels.

Abstract

Real-time markers for kidney cell damage most consistently report high diagnostic accuracy when measured in the appropriate clinical context and time window. Urinary neutrophil gelatinase-associated lipocalin (NGAL) rises within 2–6 hours after injury, with studies reporting area under the curve (AUC) values up to 0.91, sensitivities of 84–100%, and specificities as high as 99.5%. Urinary [TIMP‑2]·[IGFBP7] is typically measured at 6–12 hours post-injury and shows AUC values ranging from 0.70 to 0.94, with reported sensitivities of 42–96% and specificities up to 95%. Urinary kidney injury molecule‑1 (KIM‑1) measured at 12–24 hours post-insult yields moderate performance (AUC 0.66–0.84) with sensitivities between 75.9% and 91.6% and specificities up to 95.2%.

When combined in multi-marker panels, these biomarkers often achieve superior performance. For example, panels including NGAL, [TIMP‑2]·[IGFBP7], and cystatin C have produced AUC values as high as 0.98, while combinations with additional markers have reached similar levels of discrimination. These findings, drawn from a diverse set of populations and clinical settings—ranging from cardiac surgery and intensive care to emergency presentations—support the use of NGAL, [TIMP‑2]·[IGFBP7], and KIM‑1 as effective real-time indicators of kidney cell damage.

Methods

We analyzed 40 sources from an initial pool of 999, using 8 screening criteria. Each paper was reviewed for 5 key aspects that mattered most to the research question.

Papers identified with Elicit search

• n = 999

Papers screened using: Real-time Biomarker Detection, Human Participants, Diagnostic Performance Assessment, Appropriate Study Design, Comparative Assessment, Clinical Study Setting, Primary Research Quality, Clinical Utility Assessment

• n = 999

Papers screened out

• n = 959

Papers included for extraction

• n = 40

Screening

We screened in sources based on their abstracts that met these criteria:

• Real-time Biomarker Detection: Does this study evaluate biomarkers that can detect kidney cell damage in real-time or near real-time (within 24 hours maximum)?
• Human Participants: Does this study involve human participants with actual or potential kidney cell damage?
• Diagnostic Performance Assessment: Does this study assess diagnostic performance metrics (such as sensitivity, specificity, positive/negative predictive values, AUC, or diagnostic accuracy)?
• Appropriate Study Design: Is this study a prospective cohort study, cross-sectional diagnostic accuracy study, randomized controlled trial, systematic review, or meta-analysis?
• Comparative Assessment: Does this study compare novel biomarkers to established reference standards or other biomarkers?
• Clinical Study Setting: Is this study conducted in a clinical setting (not exclusively animal studies, in vitro studies, or cell culture studies)?
• Primary Research Quality: Is this study a primary research study or high-quality secondary research (not a case report, case series, editorial, or opinion piece)?
• Clinical Utility Assessment: Does this study assess the diagnostic performance or clinical utility of biomarkers (not just measure biomarker levels without performance evaluation)?

We considered all screening questions together and made a holistic judgement about whether to screen in each paper.

Data extraction

We asked a large language model to extract each data column below from each paper. We gave the model the extraction instructions shown below for each column.

• Study design:
  • Specify the exact type of study design used. Look in the methods section for precise description. Possible types include:
    • Cross-sectional study
    • Prospective multicenter study
    • Case-control study
    • Cohort study
• Participant characteristics:
  • Extract the following details:
    • Total number of participants
    • Patient population (e.g., critically ill patients, cardiac surgery patients)
    • Age range or mean age
    • Gender distribution
    • Specific inclusion/exclusion criteria
• Biomarkers investigated:
  • List ALL biomarkers examined in the study for detecting kidney cell damage.
    • Full name of biomarker
    • Source of biomarker (e.g., urine, serum)
    • Measurement method (if specified)
• Biomarker performance metrics:
  • Extract quantitative performance data for biomarkers, specifically:
    • Area Under the Curve (AUC)
    • Sensitivity
    • Specificity
    • Confidence intervals (if reported)
    • Cutoff values used
• Primary outcome definition:
  • Describe how acute kidney injury (AKI) was defined and diagnosed in the study.

Results

Characteristics of Included Studies

Summary of Study Characteristics:

• Most commonly evaluated biomarkers:
  • Neutrophil gelatinase-associated lipocalin (NGAL):29 studies
  • Kidney injury molecule-1 (KIM-1):21 studies
  • [TIMP-2]·[IGFBP7]:12 studies
  • Interleukin-18 (IL-18):11 studies
  • Liver-type fatty acid-binding protein (L-FABP):8 studies
  • Cystatin C (CysC):14 studies
  • Other biomarkers(e.g., NAG, B2M, SCr/creatinine, albumin, clusterin, GSTs, HGF, microRNAs, etc.): each evaluated in 1–4 studies
• Study populations:
  • Critically ill:7 studies
  • Intensive care unit (ICU):6 studies
  • Cardiac surgery:7 studies
  • Emergency department (ED)/emergency room (ER):6 studies
  • Pediatric:2 studies
  • Transplant:3 studies
  • Drug-induced/nephrotoxic settings:5 studies
  • Hospitalized (general):2 studies
  • Other populations(diabetes, heart failure, poisoning, snake envenoming, neonates, abdominal aortic aneurysm (AAA) surgery, at-risk adults, etc.): 1 study each
• AKI definitions used:
  • KDIGO:15 studies
  • AKIN:8 studies
  • RIFLE:3 studies
  • Serum creatinine (SCr) increase (not otherwise specified):3 studies
  • GFR-based:1 study
  • No mention found of a clearly specified AKI definition:13 studies
• Primary outcomes:
  • Early AKI detection/diagnosis:19 studies
  • Prediction (including risk stratification, prediction of AKI, or progression):13 studies
  • Prognosis (renal recovery, adverse events, delayed graft function):6 studies
  • Drug-induced AKI detection:5 studies
  • Other outcomes(biomarker timing/performance, diabetic nephropathy, etc.): 3 studies
• Heterogeneity:There was no consistent AKI definition across all studies. Several studies evaluated multiple biomarkers or included multiple populations or outcomes.

Individual Biomarker Performance

Summary of Diagnostic Performance:

• Area Under the Curve (AUC):
  • 5 biomarkers had minimum AUC values in the poor range (<0.7)
  • 8 biomarkers had AUC values in the fair range (0.7–0.8)
  • 8 biomarkers had AUC values in the good range (0.8–0.9)
  • 5 biomarkers had maximum AUC values in the excellent range (≥0.9)
• Sensitivity:
  • 2 biomarkers had minimum sensitivity values <50%
  • 2 biomarkers had sensitivity values in the 50–69% range
  • 7 biomarkers had sensitivity values in the 70–89% range
  • 5 biomarkers had maximum sensitivity values ≥90%
• Specificity:
  • 5 biomarkers had minimum specificity values in the 50–69% range
  • 7 biomarkers had specificity values in the 70–89% range
  • 7 biomarkers had maximum specificity values ≥90%

Combined Biomarker Performance

Several studies reported that combinations of biomarkers outperformed individual markers. Key findings from these studies include:

• Dong et al., 2017: Combination of NGAL, IL-18, and [TIMP-2] yielded an AUC of 0.973 at 12 hours post-cardiopulmonary bypass, higher than any single marker.
• Che et al., 2010: Combination of five biomarkers (cystatin C, NGAL, IL-18, retinol-binding protein, NAG) achieved an AUC of 0.98.
• Gupta et al., 2025: Combination of [TIMP-2]·[IGFBP7], NGAL, and cystatin C yielded an AUC of 0.92, which improved to 0.94 when clinical risk factors were included.
• Elmedany et al., 2017: Combination of NGAL, KIM-1, and urine sediment yielded an AUC of 0.906.

Timing of Detection and Clinical Context

Factors Affecting Biomarker Performance

• Patient population: Performance varied by clinical context, including ICU, surgery, sepsis, drug-induced injury, pediatric, oncology, and neonates.
• Comorbidities: Some markers, such as [TIMP-2]·[IGFBP7], were reported to be robust across comorbidities (chronic kidney disease, congestive heart failure, diabetes), while others may be confounded.
• Timing: Early sampling was critical. NGAL typically rose first, followed by [TIMP-2]·[IGFBP7], KIM-1, and others.
• AKI definition: Variability in AKI definitions (KDIGO, AKIN, RIFLE, expert adjudication) affected comparability across studies.
• Analytical methods: Differences in assay platforms, normalization (e.g., to creatinine), and cutoff values impacted reported performance.
• Clinical variables: Integration with clinical risk factors and scoring systems improved predictive accuracy.

References

• Ravi J. Desai, C. Kazarov, Adrian Wong, S. Kane‐Gill (2022). Kidney Damage and Stress Biomarkers for Early Identification of Drug-Induced Kidney Injury: A Systematic Review. Drug Safety
• W. Han, S. Waikar, A. Johnson, R. Betensky, C. Dent, and 2 more (2008). Urinary biomarkers in the early diagnosis of acute kidney injury. Kidney International
• Sarah Jarvis (2011). The ROMA (Risk of Ovarian Malignancy Algorithm) for estimating the risk of epithelial ovarian cancer in women presenting with pelvic mass: is it really useful? Kidney International