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Mitra R, Tholouli E, Rajai A, Saha A, Mitra S, Mitsides N. Urinary L-FABP Assay in the Detection of Acute Kidney Injury following Haematopoietic Stem Cell Transplantation. J Pers Med 2024; 14:1046. [PMID: 39452553 PMCID: PMC11508925 DOI: 10.3390/jpm14101046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2024] [Revised: 09/13/2024] [Accepted: 10/06/2024] [Indexed: 10/26/2024] Open
Abstract
Background: Acute Kidney Injury (AKI) is a condition that affects a significant proportion of acutely unwell patients and is associated with a high mortality rate. Patients undergoing haemopoietic stem cell transplantation (HSCT) are in an extremely high group for AKI. Identifying a biomarker or panel of markers that can reliably identify at-risk individuals undergoing HSCT can potentially impact management and outcomes. Early identification of AKI can reduce its severity and improve prognosis. We evaluated the urinary Liver type fatty acid binding protein (L-FABP), a tubular stress and injury biomarker both as an ELISA and a point of care (POC) assay for AKI detection in HSCT. Methods: 85 patients that had undergone autologous and allogenic HSCT (35 and 50, respectively) had urinary L-FABP (uL-FABP) measured by means of a quantitative ELISA and a semi-quantitative POC at baseline, day 0 and 7 post-transplantation. Serum creatinine (SCr) was also measured at the same time. Patients were followed up for 30 days for the occurrence of AKI and up to 18 months for mortality. The sensitivity and specificity of uL-FABP as an AKI biomarker were evaluated and compared to the performance of sCr using ROC curve analysis and logistic regression. Results: 39% of participants developed AKI within 1 month of their transplantation. The incidence of AKI was higher in the allogenic group than in the autologous HTSC group (57% vs. 26%, p = 0.008) with the median time to AKI being 25 [range 9-30] days. This group was younger (median age 59 vs. 63, p < 0.001) with a lower percentage of multiple myeloma as the primary diagnosis (6% vs. 88%, p < 0.001). The median time to AKI diagnosis was 25 [range 9-30] days. uL-FABP (mcg/gCr) at baseline, day of transplant and on the 7th day post-transplant were 1.61, 5.39 and 10.27, respectively, for the allogenic group and 0.58, 4.36 and 5.14 for the autologous group. Both SCr and uL-FABP levels rose from baseline to day 7 post-transplantation, while the AUC for predicting AKI for baseline, day 0 and day 7 post-transplant was 0.54, 0.59 and 0.62 for SCr and for 0.49, 0.43 and 0.49 uL-FABP, respectively. Univariate logistic regression showed the risk of AKI to be increased in patients with allogenic HSCT (p = 0.004, 95%CI [0.1; 0.65]) and in those with impaired renal function at baseline (p = 0.01, 95%CI [0.02, 0.54]). The risk of AKI was also significantly associated with SCr levels on day 7 post-transplant (p = 0.03, 95%CI [1; 1.03]). Multivariate logistic regression showed the type of HSCT to be the strongest predictor of AKI at all time points, while SCr levels at days 0 and 7 also correlated with increased risk in the model that included uL-FABP levels at the corresponding time points. The POC device for uL-FABP measurement correlated with ELISA (p < 0.001, Spearman 'correlation' = 0.54) Conclusions: The urinary biomarker uL-FABP did not demonstrate an independent predictive value in the detection of AKI at all stages. The most powerful risk predictor of AKI in this setting appears to be allograft recipients and baseline renal impairment, highlighting the importance of clinical risk stratification. Urinary L-FAPB as a POC biomarker was comparable to ELISA, which provides an opportunity for simple and rapid testing. However, the utility of LFABP in AKI is unclear and needs further exploration. Whether screening through rapid testing of uL-FABP can prevent or reduce AKI severity is unknown and merits further studies.
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Affiliation(s)
- Roshni Mitra
- Department of Haemato-Oncology, St Bartholomew’s Hospital, Barts Health NHS Trust, London EC1A 7BE, UK
| | - Eleni Tholouli
- Department of Haematology, Manchester University Hospitals, Oxford Road, Manchester M13 9WL, UK;
| | - Azita Rajai
- Research and Innovation, Manchester University Hospitals, Oxford Road, Manchester M13 9WL, UK
| | - Ananya Saha
- Manchester Academy of Health Sciences, Manchester University Hospitals, University of Manchester, Manchester M13 9WL, UK (S.M.)
| | - Sandip Mitra
- Manchester Academy of Health Sciences, Manchester University Hospitals, University of Manchester, Manchester M13 9WL, UK (S.M.)
| | - Nicos Mitsides
- Medical School, University of Cyprus, Nicosia 2029, Cyprus
- Nephrology Department, Nicosia General Hospital, Nicosia 2029, Cyprus
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Sun T, Yue X, Zhang G, Lin Q, Chen X, Huang T, Li X, Liu W, Tao Z. AKIML pred: An interpretable machine learning model for predicting acute kidney injury within seven days in critically ill patients based on a prospective cohort study. Clin Chim Acta 2024; 559:119705. [PMID: 38702035 DOI: 10.1016/j.cca.2024.119705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Revised: 03/29/2024] [Accepted: 04/29/2024] [Indexed: 05/06/2024]
Abstract
BACKGROUND Early recognition and timely intervention for AKI in critically ill patients were crucial to reduce morbidity and mortality. This study aimed to use biomarkers to construct a optimal machine learning model for early prediction of AKI in critically ill patients within seven days. METHODS The prospective cohort study enrolled 929 patients altogether who were admitted in ICU including 680 patients in training set (Jiefang Campus) and 249 patients in external testing set (Binjiang Campus). After performing strict inclusion and exclusion criteria, 421 patients were selected in training set for constructing predictive model and 167 patients were selected in external testing for evaluating the predictive performance of resulting model. Urine and blood samples were collected for kidney injury associated biomarkers detection. Baseline clinical information and laboratory data of the study participants were collected. We determined the average prediction efficiency of six machine learning models through 10-fold cross validation. RESULTS In total, 78 variables were collected when admission in ICU and 43 variables were statistically significant between AKI and non-AKI cohort. Then, 35 variables were selected as independent features for AKI by univariate logistic regression. Spearman correlation analysis was used to remove two highly correlated variables. Three ranking methods were used to explore the influence of 33 variables for further determining the best combination of variables. The gini importance ranking method was found to be applicable for variables filtering. The predictive performance of AKIMLpred which constructed by the XGBoost algorithm was the best among six machine learning models. When the AKIMLpred included the nine features (NGAL, IGFBP7, sCysC, CAF22, KIM-1, NT-proBNP, IL-6, IL-18 and L-FABP) with the highest influence ranking, its model had the best prediction performance, with an AUC of 0.881 and an accuracy of 0.815 in training set, similarly, with an AUC of 0.889 and an accuracy of 0.846 in validation set. Moreover, the performace was slightly outperformed in testing set with an AUC of 0.902 and an accuracy of 0.846. The SHAP algorithm was used to interpret the prediction results of AKIMLpred. The web-calculator of AKIMLpred was shown for predicting AKI with more convenient(https://www.xsmartanalysis.com/model/list/predict/model/html?mid=8065&symbol=11gk693982SU6AE1ms21). AKIMLpred was better than the optimal model built with only routine tests for predicting AKI in critically ill patients within 7 days. CONCLUSION The model AKIMLpred constructed by the XGBoost algorithm with selecting the nine most influential biomarkers in the gini importance ranking method had the best performance in predicting AKI in critically ill patients within 7 days. This data-driven predictive model will help clinicians to make quick and accurate diagnosis.
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Affiliation(s)
- Tao Sun
- The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.
| | - Xiaofang Yue
- The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.
| | - Gong Zhang
- The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.
| | - Qinyan Lin
- The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Xiao Chen
- The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.
| | - Tiancha Huang
- The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Xiang Li
- The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Weiwei Liu
- The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.
| | - Zhihua Tao
- The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China.
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Gaffar S, Aathirah AS. Fatty-Acid-Binding Proteins: From Lipid Transporters to Disease Biomarkers. Biomolecules 2023; 13:1753. [PMID: 38136624 PMCID: PMC10741572 DOI: 10.3390/biom13121753] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Revised: 10/20/2023] [Accepted: 10/26/2023] [Indexed: 12/24/2023] Open
Abstract
Fatty-acid-binding proteins (FABPs) serve a crucial role in the metabolism and transport of fatty acids and other hydrophobic ligands as an intracellular protein family. They are also recognized as a critical mediator in the inflammatory and ischemic pathways. FABPs are found in a wide range of tissues and organs, allowing them to contribute to various disease/injury developments that have not been widely discussed. We have collected and analyzed research journals that have investigated the role of FABPs in various diseases. Through this review, we discuss the findings on the potential of FABPs as biomarkers for various diseases in different tissues and organs, looking at their expression levels and their roles in related diseases according to available literature data. FABPs have been reported to show significantly increased expression levels in various tissues and organs associated with metabolic and inflammatory diseases. Therefore, FABPs are a promising novel biomarker that needs further development to optimize disease diagnosis and prognosis methods along with previously discovered markers.
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Affiliation(s)
- Shabarni Gaffar
- Graduate School, Padjadjaran University, Bandung 40132, Indonesia;
- Department of Chemistry, Faculty of Mathematics and Natural Sciences, Padjadjaran University, Sumedang 45363, Indonesia
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Motwani SS, Kaur SS, Kitchlu A. Cisplatin Nephrotoxicity: Novel Insights Into Mechanisms and Preventative Strategies. Semin Nephrol 2023; 42:151341. [PMID: 37182407 DOI: 10.1016/j.semnephrol.2023.151341] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
Cisplatin is a highly effective chemotherapeutic agent that has been used for more than 50 years for a variety of cancers; however, its use is limited by toxicity, including nephrotoxicity. In this in-depth review, we discuss the incidence of cisplatin-associated acute kidney injury, as well as common risk factors for its development. Cisplatin accumulates in the kidney tubules and causes AKI through various mechanisms, including DNA damage, oxidative stress, and apoptosis. We also discuss the spectrum of nephrotoxicity, including acute and chronic impairment of kidney function, electrolyte disturbances, and thrombotic microangiopathy. We discuss the limited options for the diagnosis, prevention, and management of these complications, along with factors that may impact future therapy with or without cisplatin. We conclude with directions for future research in this expanding and important area.
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Affiliation(s)
- Shveta S Motwani
- Division of Nephrology, Lahey Hospital and Medical Center, Burlington, MA.
| | - Sharneet Sandhu Kaur
- Division of Nephrology, Department of Medicine, University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Abhijat Kitchlu
- Division of Nephrology, Department of Medicine, University Health Network, University of Toronto, Toronto, Ontario, Canada
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Liu Y, Guan X, Shao Y, Zhou J, Huang Y. The Molecular Mechanism and Therapeutic Strategy of Cardiorenal Syndrome Type 3. Rev Cardiovasc Med 2023; 24:52. [PMID: 39077418 PMCID: PMC11273121 DOI: 10.31083/j.rcm2402052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Revised: 11/17/2022] [Accepted: 11/22/2022] [Indexed: 07/31/2024] Open
Abstract
Cardiorenal syndrome type 3 (CRS3) is defined as acute kidney injury (AKI)-induced acute cardiac dysfunction, characterized by high morbidity and mortality. CRS3 often occurs in elderly patients with AKI who need intensive care. Approximately 70% of AKI patients develop into CRS3. CRS3 may also progress towards chronic kidney disease (CKD) and chronic cardiovascular disease (CVD). However, there is currently no effective treatment. Although the major intermediate factors that can mediate cardiac dysfunction remain elusive, recent studies have summarized the AKI biomarkers, identified direct mechanisms, including mitochondrial dysfunction, inflammation, oxidative stress, apoptosis and activation of the sympathetic nervous system (SNS) and renin-angiotensin-aldosterone system (RAAS), inflammasome, as well as indirect mechanisms such as fluid overload, electrolyte imbalances, acidemia and uremic toxins, which are involved in the pathophysiological changes of CRS3. This study reviews the main pathological characteristics, underlying molecular mechanisms, and potential therapeutic strategies of CRS3. Mitochondrial dysfunction and inflammatory factors have been identified as the key initiators and abnormal links between the impaired heart and kidney, which contribute to the formation of a vicious circle, ultimately accelerating the progression of CRS3. Therefore, targeting mitochondrial dysfunction, antioxidants, Klotho, melatonin, gene therapy, stem cells, exosomes, nanodrugs, intestinal microbiota and Traditional Chinese Medicine may serve as promising therapeutic approaches against CRS3.
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Affiliation(s)
- Yong Liu
- Department of Nephrology, The Key Laboratory for the Prevention and Treatment of Chronic Kidney Disease of Chongqing, Chongqing Clinical Research Center of Kidney and Urology Diseases, Xinqiao Hospital, Army Medical University (Third Military Medical University), 400037 Chongqing, China
| | - Xu Guan
- Department of Nephrology, The Key Laboratory for the Prevention and Treatment of Chronic Kidney Disease of Chongqing, Chongqing Clinical Research Center of Kidney and Urology Diseases, Xinqiao Hospital, Army Medical University (Third Military Medical University), 400037 Chongqing, China
| | - Yuming Shao
- Medical Division, Xinqiao Hospital, Army Medical University, 400037 Chongqing, China
| | - Jie Zhou
- Department of Oncology, Southwest Cancer Center, Southwest Hospital, Army Medical University, 400038 Chongqing, China
| | - Yinghui Huang
- Department of Nephrology, The Key Laboratory for the Prevention and Treatment of Chronic Kidney Disease of Chongqing, Chongqing Clinical Research Center of Kidney and Urology Diseases, Xinqiao Hospital, Army Medical University (Third Military Medical University), 400037 Chongqing, China
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