Serum Neutrophil Gelatinase-Associated Lipocalin (NGAL) Could Provide Better Accuracy Than Creatinine in Predicting Acute Kidney Injury Development in Critically Ill Patients

Urine specific gravity, pyuria, and neutrophil gelatinase-associated lipocalin for identifying urinary tract infection in young children

BACKGROUND

To determine whether urine neutrophil gelatinase-associated lipocalin (uNGAL) might be superior to pyuria for detecting urinary tract infection (UTI) regardless of urine specific gravity (SG) in young children.

METHODS

We conducted a retrospective analysis of children aged < 3 years who were evaluated for UTI with urinalysis, urine culture, and uNGAL measurements during a 5-year period. Sensitivity, specificity, likelihood ratios (LRs), predictive values (PVs), area under the curves (AUCs) of uNGAL cut-off levels, and various microscopic pyuria thresholds for detecting UTI were calculated for dilute (SG < 1.015) and concentrated urine (SG ≥ 1.015).

RESULTS

Of 456 children included, 218 had UTI. The diagnostic value of urine white blood cell (WBC) concentration to define UTI changed with urine SG. For detecting UTI, uNGAL cut-off of 68.4 ng/mL had higher AUC values than pyuria ≥ 5 WBCs/high power field (HPF) for dilute and concentrated urine samples (both P < 0.05). Positive LR and PV and specificity of uNGAL were all greater than those of pyuria ≥ 5 WBCs/HPF regardless of urine SG, although the sensitivity of pyuria ≥ 5 WBCs/HPF was higher than that of uNGAL cut-off for dilute urine (93.8% vs. 83.5%) (P < 0.05). At uNGAL ≥ 68.4 ng/mL and ≥ 5 WBCs/HPF, posttest probabilities of UTI were 68.8% and 57.5% for dilute urine and 73.4% and 57.3% for concentrated urine, respectively.

CONCLUSIONS

Urine SG can affect the diagnostic performance of pyuria for detecting UTI and uNGAL might be helpful for identifying UTI regardless of urine SG in young children. A higher resolution version of the Graphical abstract is available as Supplementary information.

Identification and validation of the diagnostic signature associated with immune microenvironment of acute kidney injury based on ferroptosis-related genes through integrated bioinformatics analysis and machine learning

Background: Acute kidney injury (AKI) is a common and severe disease, which poses a global health burden with high morbidity and mortality. In recent years, ferroptosis has been recognized as being deeply related to Acute kidney injury. Our aim is to develop a diagnostic signature for Acute kidney injury based on ferroptosis-related genes (FRGs) through integrated bioinformatics analysis and machine learning. Methods: Our previously uploaded mouse Acute kidney injury dataset GSE192883 and another dataset, GSE153625, were downloaded to identify commonly expressed differentially expressed genes (coDEGs) through bioinformatic analysis. The FRGs were then overlapped with the coDEGs to identify differentially expressed FRGs (deFRGs). Immune cell infiltration was used to investigate immune cell dysregulation in Acute kidney injury. Functional enrichment analysis and protein-protein interaction network analysis were applied to identify candidate hub genes for Acute kidney injury. Then, receiver operator characteristic curve analysis and machine learning analysis (Lasso) were used to screen for diagnostic markers in two human datasets. Finally, these potential biomarkers were validated by quantitative real-time PCR in an Acute kidney injury model and across multiple datasets. Results: A total of 885 coDEGs and 33 deFRGs were commonly identified as differentially expressed in both GSE192883 and GSE153625 datasets. In cluster 1 of the coDEGs PPI network, we found a group of 20 genes clustered together with deFRGs, resulting in a total of 48 upregulated hub genes being identified. After ROC analysis, we discovered that 25 hub genes had an area under the curve (AUC) greater than 0.7; Lcn2, Plin2, and Atf3 all had AUCs over than this threshold in both human datasets GSE217427 and GSE139061. Through Lasso analysis, four hub genes (Lcn2, Atf3, Pir, and Mcm3) were screened for building a nomogram and evaluating diagnostic value. Finally, the expression of these four genes was validated in Acute kidney injury datasets and laboratory investigations, revealing that they may serve as ideal ferroptosis markers for Acute kidney injury. Conclusion: Four hub genes (Lcn2, Atf3, Pir, and Mcm3) were identified. After verification, the signature's versatility was confirmed and a nomogram model based on these four genes effectively distinguished Acute kidney injury samples. Our findings provide critical insight into the progression of Acute kidney injury and can guide individualized diagnosis and treatment.

Neutrophil Gelatinase-Associated Lipocalin (NGAL) in Patients with ST-Elevation Myocardial Infarction and Its Association with Acute Kidney Injury and Mortality

Neutrophil gelatinase-associated lipocalin (NGAL) is an inflammatory biomarker related to acute kidney injury (AKI). Including 1892 consecutive patients with ST-elevation myocardial infarction (STEMI), in which NGAL was measured in 1624 (86%) on admission and in a consecutive subgroup at 6-12 h (n = 163) and 12-24 h (n = 222) after admission, this study aimed to evaluate the prognostic value of NGAL in predicting AKI and mortality. Patients were stratified based on whether their admission NGAL plasma concentration was greater than or equal to/less than the median. The primary endpoint was a composite of the first occurrence of AKI or all-cause death within 30 days. AKI was classified by the maximal plasma creatinine increase from baseline during index admission as KDIGO1 (<200% increase) or KDIGO23 (≥200% increase) according to the Kidney Disease Improving Global Outcomes (KDIGO) system. Admission NGAL > the median was independently associated with a higher risk of severe AKI (KDIGO2-3) and 30-day all-cause mortality when adjusted for age, admission systolic blood pressure and high-sensitivity C-reactive protein, left-ventricular ejection fraction, known kidney dysfunction, and cardiogenic shock with an odds ratio (95% confidence interval) of 2.26 (1.18-4.51), p = 0.014. Finally, we observed increasing predictive values in a subgroup during the first day of hospitalization suggesting that assessment of NGAL should be delayed for optimal prognostic purposes.

Could melatonin prevent Vancomycin-induced nephrotoxicity in critically ill patients? A randomized, double-blinded controlled trial

Background

Previous research showed some clinical benefits regarding the nephroprotective effect of melatonin. So, this study aimed to evaluate the beneficial effect of oral melatonin on preventing acute kidney injury (AKI) in patients who received vancomycin therapy in the intensive care unit (ICU).

Methods

We performed a randomized, double-blinded, placebo-controlled pilot study in an academic hospital. Adult patients admitted to the ICU who received vancomycin with normal gastrointestinal and kidney function were randomized into treatment or placebo groups. After that, enrolled patients received a tablet of melatonin (3 mg) or placebo twice daily for seven consecutive days. The occurrence of AKI was assessed by RIFLE criteria (by measurement of serum creatinine (SCr)) and plasma neutrophil gelatinase-associated lipocalin (NGAL) concentration. Moreover, other data related to renal functions and SOFA were also compared between groups.

Results

A total of 90 patients were included in the study, while 21patients in the placebo group and 20 in the intervention group completed the study. There were no significant differences between groups regarding baseline SCr, BUN, urine output, NGAL, SOFA, and glomerular filtration rate (GFR). Our results showed that these differences remained insignificant after a 7-day follow-up between groups. However, the incidence of AKI was significantly lower in the melatonin group based on the NGAL cutoff (> 150 ng/mL).

Conclusion

We detected a significant decrease in vancomycin-induced nephrotoxicity incidence in patients receiving melatonin compared to placebo. However, more clinical trials in a larger population were required to confirm this result.

Comparative accuracy of biomarkers for the prediction of hospital-acquired acute kidney injury: a systematic review and meta-analysis

Background

Several biomarkers have been proposed to predict the occurrence of acute kidney injury (AKI); however, their efficacy varies between different trials. The aim of this study was to compare the predictive performance of different candidate biomarkers for AKI.

Methods

In this systematic review, we searched PubMed, Medline, Embase, and the Cochrane Library for papers published up to August 15, 2022. We selected all studies of adults (> 18 years) that reported the predictive performance of damage biomarkers (neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), liver-type fatty acid-binding protein (L-FABP)), inflammatory biomarker (interleukin-18 (IL-18)), and stress biomarker (tissue inhibitor of metalloproteinases-2 × insulin-like growth factor-binding protein-7 (TIMP-2 × IGFBP-7)) for the occurrence of AKI. We performed pairwise meta-analyses to calculate odds ratios (ORs) and 95% confidence intervals (CIs) individually. Hierarchical summary receiver operating characteristic curves (HSROCs) were used to summarize the pooled test performance, and the Grading of Recommendations, Assessment, Development and Evaluations criteria were used to appraise the quality of evidence.

Results

We identified 242 published relevant studies from 1,803 screened abstracts, of which 110 studies with 38,725 patients were included in this meta-analysis. Urinary NGAL/creatinine (diagnostic odds ratio [DOR] 16.2, 95% CI 10.1–25.9), urinary NGAL (DOR 13.8, 95% CI 10.2–18.8), and serum NGAL (DOR 12.6, 95% CI 9.3–17.3) had the best diagnostic accuracy for the risk of AKI. In subgroup analyses, urinary NGAL, urinary NGAL/creatinine, and serum NGAL had better diagnostic accuracy for AKI than urinary IL-18 in non-critically ill patients. However, all of the biomarkers had similar diagnostic accuracy in critically ill patients. In the setting of medical and non-sepsis patients, urinary NGAL had better predictive performance than urinary IL-18, urinary L-FABP, and urinary TIMP-2 × IGFBP-7: 0.3. In the surgical patients, urinary NGAL/creatinine and urinary KIM-1 had the best diagnostic accuracy. The HSROC values of urinary NGAL/creatinine, urinary NGAL, and serum NGAL were 91.4%, 85.2%, and 84.7%, respectively.

Conclusions

Biomarkers containing NGAL had the best predictive accuracy for the occurrence of AKI, regardless of whether or not the values were adjusted by urinary creatinine, and especially in medically treated patients. However, the predictive performance of urinary NGAL was limited in surgical patients, and urinary NGAL/creatinine seemed to be the most accurate biomarkers in these patients. All of the biomarkers had similar predictive performance in critically ill patients.

Trial registrationCRD42020207883, October 06, 2020.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-022-04223-6.

Adiponectin is unrelated to kidney function or injury markers in renal transplant recipients: A one-year follow-up study

BACKGROUND

Adiponectin (ADPN) is a biologically active cytokine produced by adipose tissue. This protein exhibits anti-inflammatory, antioxidant, antifibrotic, and insulin-sensitizing properties. As ADPN is primarily eliminated by the kidneys, it is a potential biomarker of chronic kidney disease progression. This study aimed to analyze the fluctuations in ADPN levels after kidney transplantation during a one-year follow-up and to compare them to significant renal (eGFR, NGAL) and metabolic (insulin, glucose, lipids, HOMA-IR) markers.

METHODS

Insulin, ADPN, NGAL, and basic biochemical parameters were evaluated in 51 healthy controls and 39 patients right before kidney transplantation and at five time points following transplantation (5-7 days, one month, three months, six months, and twelve months).

RESULTS

Mean ADPN levels dropped significantly right after transplantation (from 35.449 to 30.920 µg/ml, p = 0.001) and decreased gradually over a year. From the third month after the transplantation, ADPN levels were comparable to healthy individuals. At the pre-transplant time point, ADPN correlated only with insulin (r = -0.60, p < 0.001) and HOMA-IR (r = -0.55, p < 0.001). At the timepoints after transplantation, ADPN correlated only with NGAL at three months (r = -0.70, p = 0.048). The correlation of ADPN with HOMA-IR found at pre-transplant was not significant at any post-transplant time point, but at one and three months after transplant, the correlations reached a borderline significance (p = 0.07 and p = 0.08, respectively).

CONCLUSIONS

Successful kidney transplantation is followed by a gradual and significant ADPN decrease. In pre- and post-transplant patients, ADPN is unrelated to kidney function defined by GFR, but to glucose metabolism. Most of the analyzed metabolic and kidney parameters, apart from NGAL, stabilize within three months after transplantation.