# 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`

## 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

If multiple design elements are present, list all. If design is not clearly stated, write “design not clearly specified”.

### 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

# 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), 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)                 | 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 CPB               | MMP-9, NAG, KIM-1                                         | >50% serum creatinine (SCr) increase in 48h      | Early AKI diagnosis                                      |
| Jarvis, 2011                         | ICU patients (n=529)                                     | GGT, AP, NGAL, CysC, KIM-1, IL-18                          | No mention found                                  | Diagnosis/prediction of AKI, dialysis, death             |
| Koyner et al., 2010                  | Cardiac surgery (n=123)                                  | 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)             | 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)                         | 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:
  - NGAL:29 studies
  - KIM-1:21 studies
  - \[TIMP-2\]·\[IGFBP7\]:12 studies
  - IL-18:11 studies
  - L-FABP:8 studies
  - CysC:14 studies
- Study populations:
  - Critically ill:7 studies
  - ICU:6 studies
  - Cardiac surgery:7 studies
  - ED/ER:6 studies
  - Pediatric:2 studies

### Summary of Diagnostic 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%              |
| Urine microscopy/sediment| 0.79–0.865                   | 6–30%              | 91–98.6%            |

### Factors Affecting Biomarker Performance

- Patient population
- Comorbidities
- Timing
- AKI definition
- Analytical methods

## References
- [Ravi J. Desai et al., 2022.](/content/review/af3de1c3-b419-4a3c-9b43-91f9fcd18e9d/source/fd444f7662f14a41bfb76146814ff22f/index.html)
- [W. Han et al., 2008.](/content/review/af3de1c3-b419-4a3c-9b43-91f9fcd18e9d/source/c31de72c532442c0bc3b98403f3d257f/index.html)
- [Sarah Jarvis, 2011.](/content/review/af3de1c3-b419-4a3c-9b43-91f9fcd18e9d/source/d76c444b78194b5ca15c0e90ac097417/index.html)
- [J. Koyner et al., 2010.](/content/review/af3de1c3-b419-4a3c-9b43-91f9fcd18e9d/source/c464d033f1154fa8937e70c714f71d57/index.html)
- [T. Nickolas et al., 2012.](/content/review/af3de1c3-b419-4a3c-9b43-91f9fcd18e9d/source/93dec66a4122447e8e27310f9b07007a/index.html)  
- [Liqun Dong et al., 2017.](/content/review/af3de1c3-b419-4a3c-9b43-91f9fcd18e9d/source/3ebe8dcc0d054172850f24561c01acba/index.html)
- [E. Macedo, R. Mehta, 2013.](/content/review/af3de1c3-b419-4a3c-9b43-91f9fcd18e9d/source/f61acb9e50aa45ec980086a537ed8ce2/index.html) 
- [M. Kimmel et al., 2016.](/content/review/af3de1c3-b419-4a3c-9b43-91f9fcd18e9d/source/4fb733bc35544909b6a214193e024d35/index.html)

*This report contains the insightful analysis of real-time kidney damage biomarkers and their clinical significance over time.*