Perioperative heart-type fatty acid binding protein is associated with acute kidney injury after cardiac surgery

Genetic or pharmacologic blockade of mPGES-2 attenuates renal lipotoxicity and diabetic kidney disease by targeting Rev-Erbα/FABP5 signaling

Diabetic kidney disease (DKD) is one of the most common complications of diabetes, and no specific drugs are clinically available. We have previously demonstrated that inhibiting microsomal prostaglandin E synthase-2 (mPGES-2) alleviated type 2 diabetes by enhancing β cell function and promoting insulin production. However, the involvement of mPGES-2 in DKD remains unclear. Here, we aimed to analyze the association of enhanced mPGES-2 expression with impaired metabolic homeostasis of renal lipids and subsequent renal damage. Notably, global knockout or pharmacological blockage of mPGES-2 attenuated diabetic podocyte injury and tubulointerstitial fibrosis, thereby ameliorating lipid accumulation and lipotoxicity. These findings were further confirmed in podocyte- or tubule-specific mPGES-2-deficient mice. Mechanistically, mPGES-2 and Rev-Erbα competed for heme binding to regulate fatty acid binding protein 5 expression and lipid metabolism in the diabetic kidney. Our findings suggest a potential strategy for treating DKD via mPGES-2 inhibition.

Fatty acid-binding protein-3 and renal function decline in patients with chronic coronary syndrome

BACKGROUND

Renal dysfunction is common in patients with coronary artery disease. Due to the shared vascular pathogenesis between the two conditions, novel biomarkers such as the fatty acid-binding protein-3 (FABP-3) have been proposed for diagnosis and prognosis prediction. This multicentre prospective cohort study investigates the association between FABP-3 and renal dysfunction.

HYPOTHESIS

We hypothesized that higher FABP-3 levels are correlated to worse renal outcome.

METHODS

Patients with chronic coronary syndrome were classified into three groups based on the initial serum FABP-3 levels. The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation was used to estimate the patient's renal function. Renal events were defined as >25% and >50% decline in estimated glomerular filtration rate (eGFR). Cox multivariable regression was employed to delineate the correlation between FABP-3 and renal dysfunction.

RESULTS

A total of 1606 subjects were included. During a mean follow-up of 35.9 months, there were 239 patients with eGFR >25% reduction and 60 patients with >50% reduction. In the Kaplan-Meier survival curve and log-rank test, increased levels of FABP-3 were significantly correlated with eGFR >25% reduction (p < .001) and >50% reduction (p < .001). Multivariate Cox regression model revealed that subjects with higher FABP-3 exhibited a greater risk of eGFR >25% reduction (Group 2: hazard ratio [HR] = 2.328, 95% confidence interval [CI] = 1.521-3.562, p < .001; Group 3: HR = 3.054, 95% CI = 1.952-4.776, p < .001) and >50% reduction (Group 3: HR = 4.838, 95% CI = 1.722-13.591, p = .003).

CONCLUSIONS

Serum FABP-3 may serve as a novel biomarker to predict eGFR decline in patients with chronic coronary syndrome.

Urinary Biomarkers in a Living Donor Kidney Transplantation Cohort—Predictive Value on Graft Function

Early non-invasive detection and prediction of graft function after kidney transplantation is essential since interventions might prevent further deterioration. The aim of this study was to analyze the dynamics and predictive value of four urinary biomarkers: kidney injury molecule-1 (KIM-1), heart-type fatty acid binding protein (H-FABP), N-acetyl-β-D-glucosaminidase (NAG), and neutrophil gelatinase-associated lipocalin (NGAL) in a living donor kidney transplantation (LDKT) cohort. Biomarkers were measured up to 9 days after the transplantation of 57 recipients participating in the VAPOR-1 trial. Dynamics of KIM-1, NAG, NGAL, and H-FABP significantly changed over the course of 9 days after transplantation. KIM-1 at day 1 and NAG at day 2 after transplantation were significant predictors for the estimated glomerular filtration rate (eGFR) at various timepoints after transplantation with a positive estimate (p < 0.05), whereas NGAL and NAG at day 1 after transplantation were negative significant predictors (p < 0.05). Multivariable analysis models for eGFR outcome improved after the addition of these biomarker levels. Several donor, recipient and transplantation factors significantly affected the baseline of urinary biomarkers. In conclusion, urinary biomarkers are of added value for the prediction of graft outcome, but influencing factors such as the timing of measurement and transplantation factors need to be considered.

Machine learning in the prediction of cardiac surgery associated acute kidney injury with early postoperative biomarkers

Purpose

To establish novel prediction models for predicting acute kidney injury (AKI) after cardiac surgery based on early postoperative biomarkers.

Patients and methods

This study enrolled patients who underwent cardiac surgery in a Chinese tertiary cardiac center and consisted of a discovery cohort (n = 452, from November 2018 to June 2019) and a validation cohort (n = 326, from December 2019 to May 2020). 43 biomarkers were screened using the least absolute shrinkage and selection operator and logistic regression to construct a nomogram model. Three tree-based machine learning models were also established: eXtreme Gradient Boosting (XGBoost), random forest (RF) and deep forest (DF). Model performance was accessed using area under the receiver operating characteristic curve (AUC). AKI was defined according to the Kidney Disease Improving Global Outcomes criteria.

Results

Five biomarkers were identified as independent predictors of AKI and were included in the nomogram: soluble ST2 (sST2), N terminal pro-brain natriuretic peptide (NT-proBNP), heart-type fatty acid binding protein (H-FABP), lactic dehydrogenase (LDH), and uric acid (UA). In the validation cohort, the nomogram achieved good discrimination, with AUC of 0.834. The machine learning models also exhibited adequate discrimination, with AUC of 0.856, 0.850, and 0.836 for DF, RF, and XGBoost, respectively. Both nomogram and machine learning models had well calibrated. The AUC of sST2, NT-proBNP, H-FABP, LDH, and UA to discriminate AKI were 0.670, 0.713, 0.725, 0.704, and 0.749, respectively. In addition, all of these biomarkers were significantly correlated with AKI after adjusting clinical confounders (odds ratio and 95% confidence interval of the third vs. the first tertile: sST2, 3.55 [2.34-5.49], NT-proBNP, 5.50 [3.54-8.71], H-FABP, 6.64 [4.11-11.06], LDH, 7.47 [4.54-12.64], and UA, 8.93 [5.46-15.06]).

Conclusion

Our study provides a series of novel predictive models and five biomarkers for enhancing the risk stratification of AKI after cardiac surgery.

Comparison of proteomic methods in evaluating biomarker-AKI associations in cardiac surgery patients

Although immunoassays are the most widely used protein measurement method, aptamer-based methods such as the SomaScan platform can quantify up to 7000 proteins per biosample, creating new opportunities for unbiased discovery. However, there is limited research comparing the consistency of biomarker-disease associations between immunoassay and aptamer-based platforms. In a substudy of the TRIBE-AKI cohort, preoperative and postoperative plasma samples from 294 patients with previous immunoassay measurements were analyzed using the SomaScan platform. Inter-platform Spearman correlations (r_s) and biomarker-AKI associations were compared across 30 preoperative and 34 postoperative immunoassay-aptamer pairs. Possible factors contributing to inter-platform differences were examined including target protein characteristics, immunoassay, and SomaScan coefficients of variation, other assay characteristics, and sample storage time. The median r_s was 0.54 (interquartile range [IQR] 0.34-0.83) in postoperative samples and 0.41 (IQR 0.21-0.69) in preoperative samples. We observed a trend of greater r_s in biomarkers with greater concentrations; the Spearman correlation between the concentration of protein and the inter-platform correlation was 0.64 in preoperative pairs and 0.53 in postoperative pairs. Of proteins measured by immunoassays, we observed significant biomarker-AKI associations for 13 proteins preop and 24 postop; of all corresponding aptamers, 8 proteins preop and 12 postop. All proteins significantly associated with AKI as measured by SomaScan were also significantly associated with AKI as measured by immunoassay. All biomarker-AKI odds ratios were significantly different (P < 0.05) between platforms in 14% of aptamer-immunoassay pairs, none of which had high (r_s > 0.50) inter-platform correlations. Although similar biomarker-disease associations were observed overall, biomarkers with high physiological concentrations tended to have the highest-confidence inter-platform operability in correlations and biomarker-disease associations. Aptamer assays provide excellent precision and an unprecedented coverage and promise for disease associations but interpretation of results should keep in mind a broad range of correlations with immunoassays.

Urinary Spermidine Predicts and Associates with In-Hospital Acute Kidney Injury after Cardiac Surgery

Acute Kidney Injury (AKI) affects up to 30% of the patients who undergo cardiac surgery (CVS) and is related to higher mortality. We aim to investigate molecular features associated with in-hospital AKI development and determine the predictive value of these features when analyzed preoperatively. This is a case-control study. From an initial cohort of 110 recruited subjects, a total of 60 patients undergoing cardiac surgery were included: 20 (33%) developed in-hospital AKI (CVS-AKI) and 40 did not (controls, CVS-C). Pre- and post-surgery samples were collected and a prospective study was carried out. A total of 312 serum samples and 258 urine samples were analyzed by nuclear magnetic resonance, mass spectrometry and ELISA. Six features predicted AKI development in pre-surgery samples: urinary kidney functional loss marker kidney injury molecule-1 (uKIM-1), 2-hydroxybutyric acid, 2-hydroxyphenylacetic acid, hippuric acid, phosphoethanolamine and spermidine. Two of them stood out as powerful predictors. Pre-surgery uKIM-1 levels were increased in CVS-AKI vs. CVS-C (AUC = 0.721, p-value = 0.0392) and associated strongly with the outcome (OR = 5.333, p-value = 0.0264). Spermidine showed higher concentration in CVS-AKI (p-value < 0.0001, AUC = 0.970) and had a strong association with the outcome (OR = 69.75, p-value < 0.0001). uKIM-1 and particularly spermidine predict in-hospital AKI associated with CVS in preoperative samples. These findings may aid in preventing postoperative AKI and improve prognosis of CVS.

Using the SYNTAX score to predict myocardial injury early after on-pump coronary artery bypass surgery: a single-centre experience analysis

Introduction

Marked isolated elevation of cardiac biomarkers (CK-MB, cardiac troponin I, heart-type fatty acid binding protein, hFABP) within 48 hours after coronary artery bypass surgery (CABG), even in the absence of electrocardiographic/angiographic evidence of myocardial infarction (MI), indicates prognostically significant cardiac procedural myocardial injury. There are no data exploring the relationship between the complexity of coronary atherosclerotic burden and early post-CABG myocardial injury.

Aim

To analyse correlations and predictive strength of the SYNTAX score (SS) for early myocardial injury after on-pump CABG.

Material and methods

One hundred and twenty consecutive patients undergoing CABG were included in the analysis. We obtained data on demographics, medical history, cardiovascular risk factors and echocardiography. Cardiac biomarkers were assessed at 6 hours after CABG. Multivariate linear regression analysis was performed to evaluate independent variables correlated with cardiac biomarkers.

Results

The most significant predictor for myocardial injury was SS, strongly correlated with the rise of all cardiac biomarkers (p < 0.001). Hypertension and creatinine clearance were associated with cTnI and hFABP. Diabetes was corelated with hFABP. In a multivariate analysis including all significant predictors, SS remained an independent predictor for myocardial injury, strongly associated with hFABP (p < 0.001, OR = 5.79, 95% CI: 3.59–7.98), cTnI (p < 0.001, OR = 6.49, 95% CI: 4.78–8.20), but not with CK-MB (95% CI: 0.61–1.07).

Conclusions

Defining myocardial injury as elevation of cardiac biomarkers between normal values and the cut-off for MI has a tremendous clinical significance as patients maintain high negative prognostic rates. SS could be used to predict post-operative rise of cardiac biomarkers, the correlation between SS and myocardial injury being very solid.

Developing Biomarker Panels to Predict Progression of Acute Kidney Injury After Cardiac Surgery

Introduction

Acute kidney injury (AKI) is a frequent complication of cardiac surgery, but only a fraction of cardiac surgery patients that experience postoperative AKI have progression to more severe stages. Biomarkers that can distinguish patients that will experience progression of AKI are potentially useful for clinical care and/or the development of therapies.

Methods

Data come from a prospective cohort study of cardiac surgery patients; the analytic dataset contained data from 354 cardiac surgery patients meeting criteria for AKI following surgery. Candidate predictors were 38 biomarkers of kidney function, insult, or injury measured at the time of AKI diagnosis. The outcome was AKI progression, defined as worsening of AKI Network stage. We investigated combining biomarkers with Bayesian model averaging (BMA) and random forests of classification trees, with and without center transformation. For both approaches, we used resampling-based methods to avoid optimistic bias in our assessment of model performance.

Results

BMA yielded a combination of 3 biomarkers and an optimism-corrected estimated area under the receiver operating characteristic curve (AUC) of 0.75 (95% confidence interval [CI]: 0.68, 0.82). The random forests approach, which nominally uses all biomarkers, had an estimated AUC of 0.74 (95% CI: 0.66, 0.82). A second application of random forests applied to biomarker values after a center-specific transformation had an estimated AUC of 0.80 (95% CI: 0.72, 0.88).

Conclusion

These findings suggest that the application of advanced statistical techniques to combine biomarkers offers only modest improvements over use of single biomarkers alone. This exemplifies a common experience in biomarker research: combinations of modestly performing biomarkers often also have modest performance.

Stratifying risk of acute kidney injury in pre and post cardiac surgery patients using a novel biomarker-based algorithm and clinical risk score

Acute kidney injury (AKI) following cardiac surgery significantly increases morbidity and mortality risks. Improving existing clinical methods of identifying patients at risk of perioperative AKI may advance management and treatment options. This study investigated whether a combination of biomarkers and clinical factors pre and post cardiac surgery could stratify patients at risk of developing AKI. Patients (n = 401) consecutively scheduled for elective cardiac surgery were prospectively studied. Clinical data was recorded and blood samples were tested for 31 biomarkers. Areas under receiver operating characteristic (AUROCs) were generated for biomarkers pre and postoperatively to stratify patients at risk of AKI. Preoperatively sTNFR1 had the highest predictive ability to identify risk of developing AKI postoperatively (AUROC 0.748). Postoperatively a combination of H-FABP, midkine and sTNFR2 had the highest predictive ability to identify AKI risk (AUROC 0.836). Preoperative clinical risk factors included patient age, body mass index and diabetes. Perioperative factors included cardio pulmonary bypass, cross-clamp and operation times, intra-aortic balloon pump, blood products and resternotomy. Combining biomarker risk score (BRS) with clinical risk score (CRS) enabled pre and postoperative assignment of patients to AKI risk categories. Combining BRS with CRS will allow better management of cardiac patients at risk of developing AKI.

Biomarkers of Acute Kidney Injury after Cardiac Surgery: A Narrative Review

Cardiac surgery-associated acute kidney injury (CSA-AKI) is a major and serious complication in patients undergoing cardiac surgery and is independently associated with perioperative mortality and mortality. Therapeutic intervention aiming at reversing kidney dysfunction seems disappointing across multiple settings. Consequently, attention has shifted from treatment to prevention and early detection. The Kidney Disease: Improving Global Outcomes (KDIGO) guidelines have unified diagnostic standards mainly based on the serum creatinine (Scr) level or urine output, but neither marker is kidney specific. Efforts have been made to identify novel biomarkers with high sensitivity and specificity. The diagnostic capabilities of neutrophil gelatinase-associated lipocalin (NGAL) and G1 cell cycle arrest biomarker as biomarkers have been confirmed in a large number of clinical trials. The utility of biomarkers of cardiac function and inflammation has been validated in clinical studies. Aiming to offer valuable information for further research, we summarize the progress in defining current markers relevant to CSA-AKI in the last three years.

Acute Kidney Injury after Cardiac Surgery: Risk Factors and Novel Biomarkers

Acute kidney injury (AKI) is a common and severe complication after cardiac surgery. Currently, a series of novel biomarkers have favored the assessment of AKI after cardiac surgery in addition to the conventional indicators. The biomartkers, such as urinary liver fatty acid binding protein (L-FABP), urinary neutrophil gelatinase-associated lipocalin (NGAL), serum L-FABP, heart-type FABP, kidney injury molecule 1 (KIM-1), and interleukin-18 were found to be significantly higher in patients who developed AKI after cardiac surgery than those who did not. Apart from urinary interleukin-18, the novel biomarkers have been recognized as reliable indicators for predicting the diagnosis, adverse outcome, and even mortality of AKI after cardiac surgery. The timing of the renal replacement therapy is a significant predictor relating to patients’ prognoses. In patients with AKI after cardiac surgery, renal replacement therapy should be performed as early as possible in order to achieve promising outcomes. In children, AKI after cardiac surgery can be managed with peritoneal dialysis. AKI after cardiac surgery has received extensive attention as it may increase early mortality and impact long-term survival of patients as well. The purpose of this article was to analyze the changes of the pertinent biomarkers, to explore the related risk factors leading to the occurrence of AKI after cardiac surgery, and to provide a basis for the clinical prevention and reduction of AKI.

The future for diagnostic tests of acute kidney injury in critical care: evidence synthesis, care pathway analysis and research prioritisation

BACKGROUND

Acute kidney injury (AKI) is highly prevalent in hospital inpatient populations, leading to significant mortality and morbidity, reduced quality of life and high short- and long-term health-care costs for the NHS. New diagnostic tests may offer an earlier diagnosis or improved care, but evidence of benefit to patients and of value to the NHS is required before national adoption.

OBJECTIVES

To evaluate the potential for AKI in vitro diagnostic tests to enhance the NHS care of patients admitted to the intensive care unit (ICU) and identify an efficient supporting research strategy.

DATA SOURCES

We searched ClinicalTrials.gov, The Cochrane Library databases, Embase, Health Management Information Consortium, International Clinical Trials Registry Platform, MEDLINE, metaRegister of Current Controlled Trials, PubMed and Web of Science databases from their inception dates until September 2014 (review 1), November 2015 (review 2) and July 2015 (economic model). Details of databases used for each review and coverage dates are listed in the main report.

REVIEW METHODS

The AKI-Diagnostics project included horizon scanning, systematic reviewing, meta-analysis of sensitivity and specificity, appraisal of analytical validity, care pathway analysis, model-based lifetime economic evaluation from a UK NHS perspective and value of information (VOI) analysis.

RESULTS

The horizon-scanning search identified 152 potential tests and biomarkers. Three tests, Nephrocheck^® (Astute Medical, Inc., San Diego, CA, USA), NGAL and cystatin C, were subjected to detailed review. The meta-analysis was limited by variable reporting standards, study quality and heterogeneity, but sensitivity was between 0.54 and 0.92 and specificity was between 0.49 and 0.95 depending on the test. A bespoke critical appraisal framework demonstrated that analytical validity was also poorly reported in many instances. In the economic model the incremental cost-effectiveness ratios ranged from £11,476 to £19,324 per quality-adjusted life-year (QALY), with a probability of cost-effectiveness between 48% and 54% when tests were compared with current standard care.

LIMITATIONS

The major limitation in the evidence on tests was the heterogeneity between studies in the definitions of AKI and the timing of testing.

CONCLUSIONS

Diagnostic tests for AKI in the ICU offer the potential to improve patient care and add value to the NHS, but cost-effectiveness remains highly uncertain. Further research should focus on the mechanisms by which a new test might change current care processes in the ICU and the subsequent cost and QALY implications. The VOI analysis suggested that further observational research to better define the prevalence of AKI developing in the ICU would be worthwhile. A formal randomised controlled trial of biomarker use linked to a standardised AKI care pathway is necessary to provide definitive evidence on whether or not adoption of tests by the NHS would be of value.

STUDY REGISTRATION

The systematic review within this study is registered as PROSPERO CRD42014013919.

FUNDING

The National Institute for Health Research Health Technology Assessment programme.

Relationship Between Level of Heart Type Fatty Acid Binding Protein (Before and after Procedures) with Acute Renal Failure after PCI in Patients Under PCI

BACKGROUND & OBJECTIVE

Acute renal failure (AKI) is one of the most important complications of PCI. Due to delay in creatinine increase, we need specific factors to detect AKI earlier. The aim of this study is to evaluate the valuable factors by focusing on HFAB-P that can be predictive for AKI after Percutaneous Coronary Intervention (PCI).

METHODS

This prospective study was performed on 95 patients (55 males and 44 females aged between 49-78 years) under PCI in Golestan and Imam Khomeini hospitals in Ahvaz. Patients were divided into three groups based on the development of AKI after the procedure: no AKI, severe AKI (doubling of serum creatinine or needing dialysis) and any type of AKI (increased creatinine ≥ 0/3 mg/dl or a 50% increase in the means of 1/5 times serum creatinine). The demographic and clinical characteristics of the patients, the medical history and the results of the HFABP marker, GFR, and creatinine before and after PCI were evaluated for all patients.

RESULTS

The progenies showed 6 patients with severe AKI, 17 patients with any type of AKI, and 72 patients without AKI. Diabetes (P = 0.003), hypertension (P = 0.027), gender of patients (P = 0.025) and hospital admission days (P <0.001) were significantly different among the groups. Patients' age and positive troponin were significantly higher in patients with AKI. HFABP was the only factor that had significant changes before and after PCI (P <0.001). The cut-off value of HFABP was 4.69 with 95.6% sensitivity and 84.7% specificity. It has a good negative predictive value of 98.39% which suggests it to be a good test for the AKI prediction. Glomerular Filtration Rate (GFR) and creatinine (Cr) were significantly different after PCI (P <0.001).

CONCLUSION

HFABP can be considered as a predictor for AKI after PCI. Moreover, our study suggests that evaluating several parameters such as Cr and GFR before and after PCI can predict the AKI development after PCI.

Update on perioperative acute kidney injury

PURPOSE OF REVIEW

In this review, we discuss the latest updates on perioperative acute kidney injury (AKI) and the specific considerations that are relevant to different surgeries and patient populations.

RECENT FINDINGS

AKI diagnosis is constantly evolving. New biomarkers detect AKI early and shed a light on the possible cause of AKI. Hypotension, even for a short duration, is associated with perioperative AKI. The debate on the deleterious effects of chloride-rich solutions is still far from conclusion. Remote ischemic preconditioning is showing promising results in the possible prevention of perioperative AKI. No definite data show a beneficiary effect of statins, fenoldepam, or sodium bicarbonate in preventing AKI.

SUMMARY

Perioperative AKI is prevalent and associated with significant morbidity and mortality. Considering the lack of effective preventive or therapeutic interventions, this review focuses on perioperative AKI: measures for early diagnosis, defining risks and possible mechanisms, and summarizing current knowledge for intraoperative fluid and hemodynamic management to reduce risk of AKI.

Development of biomarker combinations for postoperative acute kidney injury via Bayesian model selection in a multicenter cohort study

Background

Acute kidney injury (AKI) is a frequent complication of cardiac surgery. We sought prognostic combinations of postoperative biomarkers measured within 6 h of surgery, potentially in combination with cardiopulmonary bypass time (to account for the degree of insult to the kidney). We used data from a large cohort of patients and adapted methods for developing biomarker combinations to account for the multicenter design of the study.

Methods

The primary endpoint was sustained mild AKI, defined as an increase of 50% or more in serum creatinine over preoperative levels lasting at least 2 days during the hospital stay. Severe AKI (secondary endpoint) was defined as a serum creatinine increase of 100% or more or dialysis during hospitalization. Data were from a cohort of 1219 adults undergoing cardiac surgery at 6 medical centers; among these, 117 developed sustained mild AKI and 60 developed severe AKI. We considered cardiopulmonary bypass time and 22 biomarkers as candidate predictors. We adapted Bayesian model averaging methods to develop center-adjusted combinations for sustained mild AKI by (1) maximizing the posterior model probability and (2) retaining predictors with posterior variable probabilities above 0.5. We used resampling-based methods to avoid optimistic bias in evaluating the biomarker combinations.

Results

The maximum posterior model probability combination included plasma N-terminal-pro-B-type natriuretic peptide, plasma heart-type fatty acid binding protein, and change in serum creatinine from before to 0-6 h after surgery; the median probability combination additionally included plasma interleukin-6. The center-adjusted, optimism-corrected AUCs for these combinations were 0.80 (95% CI: 0.78, 0.87) and 0.81 (0.78, 0.87), respectively, for predicting sustained mild AKI, and 0.81 (0.76, 0.90) and 0.83 (0.76, 0.90), respectively, for predicting severe AKI. For these data, the Bayesian model averaging methods yielded combinations with prognostic capacity comparable to that achieved by standard frequentist methods but with more parsimonious models.

Conclusions

Pending external validation, the identified combinations could be used to identify individuals at high risk of AKI immediately after cardiac surgery and could facilitate clinical trials of renoprotective agents.

Group analysis identifies differentially elevated biomarkers with distinct outcomes for advanced acute kidney injury in cardiac surgery

AIM

To investigate early postoperative biomarkers for risk discrimination of advanced acute kidney injury (AKI).

MATERIALS & METHODS

Postoperative plasma biomarkers including NGAL, h-FABP, CK-MB, hsTNT, NT-proBNP, IL-6, IL-10 and VEGF were analyzed using group-based method among 426 patients with AKI after cardiac surgery.

RESULTS

Six patient groups with distinct biomarker patterns were identified. Individual biomarker displayed significant difference across the groups. The groups showed better discrimination for advanced AKI than any single biomarker either with or without adjusting for clinical variables. Average concentration of a single biomarker within each group, mortality and risk of a secondary outcome all demonstrated an approximately U-shaped relationship with proportion of advanced AKI within each group.

CONCLUSION

The group-based analysis revealed that the order of the patient groups with an increasing likelihood of advanced AKI had a nonlinear relationship with average concentration of an individual biomarker, mortality and risk of other outcomes.

Biomarkers for Diagnosis and Prognosis of AKI in Children: One Size Does Not Fit All

Pediatric AKI has become a significant health concern due to its rising incidence and association with adverse outcomes. Because of the limitations of serum creatinine, ongoing research has evaluated multiple novel biomarkers for the early detection of AKI. Identifying biomarkers that precede changes in serum creatinine is vital, because these biomarkers provide opportunities to improve outcomes through early diagnosis and timely disease management. In this review, we discuss salient findings on 16 candidate biomarkers and their association with AKI. We explore the differences in biomarker distribution by age and discuss why adult biomarker research findings cannot be directly extrapolated to children. With future research, more consideration needs to be given to how the maturing kidney affects biomarker levels and how we interpret biomarker performance in children. A comprehensive approach using age-specific biomarker reference ranges is required to develop pediatric biomarkers and improve outcomes for children with kidney disease.

Relationship of Kidney Injury Biomarkers with Long-Term Cardiovascular Outcomes after Cardiac Surgery

Clinical AKI, measured by serum creatinine elevation, is associated with long-term risks of adverse cardiovascular (CV) events and mortality in patients after cardiac surgery. To evaluate the relative contributions of urine kidney injury biomarkers and plasma cardiac injury biomarkers in adverse events, we conducted a multicenter prospective cohort study of 968 adults undergoing cardiac surgery. On postoperative days 1-3, we measured five urine biomarkers of kidney injury (IL-18, NGAL, KIM-1, L-FABP, and albumin) and five plasma biomarkers of cardiac injury (NT-proBNP, H-FABP, hs-cTnT, cTnI, and CK-MB). The primary outcome was a composite of long-term CV events or death, which was assessed via national health care databases. During a median 3.8 years of follow-up, 219 (22.6%) patients experienced the primary outcome (136 CV events and 83 additional deaths). Compared with patients without postsurgical AKI, patients who experienced AKI Network stage 2 or 3 had an adjusted hazard ratio for the primary composite outcome of 3.52 (95% confidence interval, 2.17 to 5.71). However, none of the five urinary kidney injury biomarkers were significantly associated with the primary outcome. In contrast, four out of five postoperative cardiac injury biomarkers (NT-proBNP, H-FABP, hs-cTnT, and cTnI) strongly associated with the primary outcome. Mediation analyses demonstrated that cardiac biomarkers explained 49% (95% confidence interval, 1% to 97%) of the association between AKI and the primary outcome. These results suggest that clinical AKI at the time of cardiac surgery is indicative of concurrent CV stress rather than an independent renal pathway for long-term adverse CV outcomes.

A comprehensive analysis and annotation of human normal urinary proteome

Biomarkers are measurable changes associated with the disease. Urine can reflect the changes of the body while blood is under control of the homeostatic mechanisms; thus, urine is considered an important source for early and sensitive disease biomarker discovery. A comprehensive profile of the urinary proteome will provide a basic understanding of urinary proteins. In this paper, we present an in-depth analysis of the urinary proteome based on different separation strategies, including direct one dimensional liquid chromatography–tandem mass spectrometry (LC/MS/MS), two dimensional LC/MS/MS, and gel-eluted liquid fraction entrapment electrophoresis/liquid-phase isoelectric focusing followed by two dimensional LC/MS/MS. A total of 6085 proteins were identified in healthy urine, of which 2001 were not reported in previous studies and the concentrations of 2571 proteins were estimated (spanning a magnitude of 10⁶) with an intensity-based absolute quantification algorithm. The urinary proteins were annotated by their tissue distribution. Detailed information can be accessed at the “Human Urine Proteome Database” (www.urimarker.com/urine).