Elicit: Real-Time Kidney Damage Markers (public)

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

Paper search

Using your research question “What are the most effective real-time markers of kidney cell damage?”, we searched across over 126 million academic papers from the Semantic Scholar corpus. We retrieved the 999 papers most relevant to the query.

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)?

Data extraction

We asked a large language model to extract each data column below from each paper.

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. Include:
- 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. Look for:
- Specific diagnostic criteria used
- Staging system (e.g., KDIGO, RIFLE)
- Specific thresholds (e.g., 50% increase in serum creatinine)
- Method of clinical adjudication

If multiple definitions or methods are used, list all. If the definition is not clearly stated, write “outcome definition not clearly specified”.

Results

Characteristics of Included Studies

Study	Study Population	Biomarkers Evaluated	AKI Definition	Primary Outcome
Bihorac et al., 2014	Critically ill patients (n=420)	Urinary [TIMP-2]·[IGFBP7]	No mention found	Prediction of moderate to severe acute kidney injury (AKI) within 12h
Piedrafita et al., 2022	Cardiac surgery (n=1170), intensive care unit (ICU) (n=1569)	Urinary peptide signature, NGAL, calprotectin, [TIMP-2]/[IGFBP7]	Kidney Disease: Improving Global Outcomes (KDIGO) 2012	Early AKI prediction (7-day KDIGO)
Sun et al., 2024	Critically ill patients (n=417+164)	12 urinary biomarkers, U-AKIpredTM (alpha-1-microglobulin, L-FABP, IGFBP7)	KDIGO	AKI within 12h of panel measurement
Desai et al., 2022	Hospitalized patients (systematic review)	Beta-2 microglobulin (B2M), IL-18, KIM-1, L-FABP, NGAL, [TIMP-2]*[IGFBP7]	No mention found	Early detection of drug-induced AKI
Han et al., 2008	Acute/chronic kidney disease, pediatric cardiopulmonary bypass (CPB)	Urinary MMP-9, NAG, KIM-1	>50% serum creatinine (SCr) increase in 48h	Early AKI diagnosis
Jarvis, 2011	ICU patients (n=529)	GGT, AP, NGAL, cystatin C (CysC), KIM-1, IL-18	No mention found	Diagnosis/prediction of AKI, dialysis, death
Koyner et al., 2010	Cardiac surgery (n=123)	Urinary NGAL, CysC, KIM-1, HGF, π-GST, α-GST	No mention found	Early detection and prognosis of AKI
Pan et al., 2022	Adults >18, mixed settings (n=38,725)	NGAL (urine/serum), KIM-1, L-FABP, IL-18, [TIMP-2]·[IGFBP7]	KDIGO, AKIN, RIFLE	Predictive performance of biomarkers for AKI
Nickolas et al., 2012	Emergency department (ED) patients (n=1,635)	Urinary NGAL, KIM-1, L-FABP, IL-18, CysC	No mention found	Diagnostic/prognostic value of biomarkers
Dong et al., 2017	Pediatric cardiac surgery (n=150)	Urinary NGAL, IL-18, L-FABP, KIM-1, [TIMP-2], IGFBP7	KDIGO (≥50% SCr increase)	Sequential biomarker elevation for AKI

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, 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

Biomarker	Area Under the Curve (AUC) Range	Sensitivity Range	Specificity Range
[TIMP-2]·[IGFBP7]	0.70–0.94	42–96%	50–95%
NGAL (urine)	0.62–0.91	55–100%	64–99.5%
KIM-1 (urine)	0.66–0.84	75.9–91.6%	61.8–95.2%
Cystatin C (urine/plasma)	0.68–0.88	71–79.3%	74.2–92%
Albumin (urine)	0.44–0.94	No mention found	No mention found
IL-18 (urine)	0.72–0.94	75–85%	63–73%
Midkine (urine)	0.88–0.96	87–97%	85–90%
Peptide signature (urine)	0.78–0.79	No mention found	No mention found
Clusterin (urine)	0.81	No mention found	No mention found
Urine microscopy/sediment	0.79–0.865	6–30%	91–98.6%

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%
- We didn’t find sensitivity data for albumin, peptide signature, or clusterin
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%
- We didn’t find specificity data for albumin, peptide signature, or clusterin
- No mention found of biomarkers with minimum specificity values <50% in the included studies.

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.

Summary:

In the studies where this was evaluated, multi-marker panels and combinations were reported to outperform single biomarkers, particularly for early detection and risk stratification.
The most effective combinations typically included [TIMP-2]·[IGFBP7] and NGAL, sometimes with cystatin C or clinical variables.

Timing of Detection and Clinical Context

Biomarker/Combination	Optimal Detection Window	Clinical Context	Performance Metrics
NGAL (urine)	2–6 hours post-injury	Pediatric CPB, sepsis, ICU	AUC >0.9 (early), Sensitivity 84–100%, Specificity 80–99.5%
[TIMP-2]·[IGFBP7]	6–12 hours post-injury	ICU, cardiac surgery, ED	AUC 0.70–0.94, Sensitivity 89–96%, Specificity 50–95%
KIM-1 (urine)	12–24 hours post-injury	Drug-induced, contrast nephropathy	AUC 0.66–0.84, Sensitivity 75.9–91.6%, Specificity 61.8–95.2%
Multi-marker panels	6–24 hours post-injury	Cardiac surgery, ICU, diverse AKI	AUC 0.906–0.98
Urine sediment	2–48 hours post-injury	Cardiac surgery, ICU	AUC 0.79–0.865, Specificity 91–98.6%

Summary of Timing and Context:

Biomarker types: Three individual biomarkers (NGAL, [TIMP-2]·[IGFBP7], KIM-1), one multi-marker panel, and one urine sediment analysis were evaluated for timing.
Optimal detection windows:
- NGAL: 2–6 hours post-injury
- [TIMP-2]·[IGFBP7]: 6–12 hours post-injury
- KIM-1: 12–24 hours post-injury
- Multi-marker panel: 6–24 hours post-injury
- Urine sediment: 2–48 hours post-injury
Performance metrics:
- AUC values for all five biomarkers/panels ranged from 0.66–0.98.
  - NGAL: AUC >0.9 (early)
  - [TIMP-2]·[IGFBP7]: AUC 0.70–0.94
  - KIM-1: AUC 0.66–0.84
  - Multi-marker panel: AUC 0.906–0.98
  - Urine sediment: AUC 0.79–0.865
- Sensitivity values were found for NGAL (84–100%), [TIMP-2]·[IGFBP7] (89–96%), and KIM-1 (75.9–91.6%).
- Specificity values were found for NGAL (80–99.5%), [TIMP-2]·[IGFBP7] (50–95%), KIM-1 (61.8–95.2%), and urine sediment (91–98.6%).
- We didn’t find sensitivity or specificity values for multi-marker panels in the included studies.

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.

Summary:

The effectiveness of real-time kidney cell damage markers was influenced by clinical context, timing, comorbidities, and methodological factors.
Standardization of definitions, timing, and analytical methods is needed to optimize and compare biomarker performance across studies.

References

Download BIBDownload RISDownload TXT

Report

Status Gather sources 999 sources found

Details Screen sources 40 sources included

Details Extract data 200 data points extracted

Details Generate report Save PDF

Report

Kidney Damage and Stress Biomarkers for Early Identification of Drug-Induced Kidney Injury: A Systematic Review

Ravi J. Desai, C. Kazarov, Adrian Wong, S. Kane‐Gill

Drug Safety·

2022· 19 citations

SourceDOI

Study design

Systematic review

Participant characteristics

Total number of participants: not reported
Patient population: hospitalized patients
Age range or mean age: not reported
Gender distribution: not reported
Specific inclusion/exclusion criteria: not reported

Biomarkers investigated

β2-microglobulin (B2M) - urine
interleukin (IL)-18 - urine
kidney injury molecule-1 (KIM-1) - urine
liver-type fatty acid-binding protein (L-FABP) - urine
neutrophil gelatinase-associated lipocalin (NGAL) - urine
tissue inhibitor of metalloproteinase-2 * insulin-like growth factor-binding protein 7 [TIMP-2]*[IGFBP-7] - urine

Biomarker performance metrics

Not mentioned (the abstract does not provide specific quantitative performance metrics for the biomarkers)

Primary outcome definition

outcome definition not clearly specified

INTRODUCTION

Acute kidney injury (AKI) resulting from nephrotoxic medication use is prominent in hospitalized patients and is attributable to overall increases in mortality and costs of care. Serum creatinine (SCr), the current standard for identifying drug-induced AKI (DIAKI) is often delayed in its response to kidney insult by 26-36 h.

OBJECTIVE

This systematic review seeks to evaluate the clinical utility of several novel kidney damage and stress biomarkers for the prediction/timely detection of DIAKI, in comparison with traditional methods.

METHODS

A systematic review of the CINAHL, Cochrane Library, Embase, and PubMed databases was conducted per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, for articles analyzing the use of β2-microglobulin (B2M), interleukin (IL)-18, kidney injury molecule-1 (KIM-1), liver-type fatty acid-binding protein (L-FABP), neutrophil gelatinase-associated lipocalin (NGAL), and tissue inhibitor of metalloproteinase-2 * insulin-like growth factor-binding protein 7 [TIMP-1]*[IGFBP-7], for identifying DIAKI. Primary outcomes included time to DIAKI diagnosis using traditional methods and the time to significant difference in biomarker concentrations between DIAKI and non-AKI study subjects. Secondary outcomes included biomarker concentrations at the time of significant difference between the AKI status groups.

RESULTS

Fifteen unique articles were identified from the literature search. Twelve studies consisted of strictly hospitalized patient populations and three studies included hospitalized patients and patients discharged to home treatment. No studies reported values for urine volume output. Seventy-three percent of studies reported earlier times to significant difference of novel biomarker concentrations between the AKI and non-AKI groups than diagnosis of DIAKI by SCr alone. Significant variation was observed for individual urine biomarker concentrations at time of significant difference between the AKI status groups.

CONCLUSIONS

All analyzed biomarkers showed potential for use as early clinical markers of DIAKI, however further consensus on threshold urine concentrations for DIAKI is needed for meaningful implementation of these biomarkers in clinical practice.