Relationship between plasma leptin levels and clinical outcomes of pediatric traumatic brain injury

Lin, Chao; Huang, Shoujiang; Wang, Ning; Shen, Zhipeng

doi:10.1016/j.peptides.2012.03.024

Cited by 15 publications

(8 citation statements)

References 35 publications

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“…However, the discriminatory power of enrollment HMGB level was lower than that of enrollment NGAL, and in contrast to NGAL, subsequent HMGB levels did not predict outcome. Leptin is a peptide expressed in neuronal tissue and extracranial adipose, and has been evaluated as a prognostic marker after traumatic brain injury (31), but did not correlate with outcome in this cohort. ICAM-1 is a marker of endothelial dysfunction and pathological central nervous system inflammatory states where it facilitates leukocyte adhesion and recruitment (32).…”

Section: Discussionmentioning

confidence: 89%

Serum Neutrophil Gelatinase–Associated Lipocalin Predicts Survival After Resuscitation From Cardiac Arrest

Elmer

Jeong

Abebe

et al. 2016

Critical Care Medicine

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Objective In the first days after cardiac arrest, accurate prognostication is challenging. Serum biomarkers are a potentially attractive adjunct for prognostication and risk stratification. Our primary objective in this exploratory study was to identify novel early serum biomarkers that predict survival after cardiac arrest earlier than currently possible. Design Prospective, observational study Setting A single academic medical center Subjects Adult subjects who sustained cardiac arrest with return of spontaneous circulation Intervention None Measurements and Main Results We obtained blood samples from each subject at enrollment, 6, 12, 24, 48 and 72 hours after return of spontaneous circulation. We measured serum levels of novel biomarkers including neutrophil gelatinase-associated lipocalin (NGAL), high-mobility group protein B1 (HMGB), intracellular cell adhesion molecule 1 (ICAM-1) and leptin, as well as previously characterized biomarkers including neuron specific enolase (NSE) and S100B protein. Our primary outcome of interest was survival to hospital discharge. We compared biomarker concentrations at each time point between survivors and non-survivors and used logistic regression to test the unadjusted associations of baseline clinical characteristics and enrollment biomarker levels with survival. Finally, we constructed a series of adjusted models to explore the independent association of each enrollment biomarker level with survival. A total of 86 subjects were enrolled. Enrollment levels of HMGB, NGAL and S100B were higher in non-survivors than survivors. Enrollment leptin, NSE and ICAM-1 levels did not differ between non-survivors and survivors. The discriminatory power of enrollment NGAL level was greatest (c-statistic 0.78 (95%CI 0.66 – 0.90), and remained stable across all time points. In our adjusted models, enrollment NGAL level was independently associated with survival even after controlling for development of acute kidney injury and its addition to clinical models improved overall predictive accuracy. Conclusion Serum NGAL levels are strongly predictive of survival to hospital discharge after cardiac arrest.

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Section: Discussionmentioning

confidence: 89%

Serum Neutrophil Gelatinase–Associated Lipocalin Predicts Survival After Resuscitation From Cardiac Arrest

Elmer

Jeong

Abebe

et al. 2016

Critical Care Medicine

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“…For the first series of models, univariate associations between clinical characteristics and PICH were tested with logistic regression. Clinical scales were dichotomized for analysis, specifically admission GCS into normal (15) versus abnormal (3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14) and Marshall score on the initial head CT into mild (1-2) injury and severe (3)(4)(5)(6) injury. Covariates that were significantly associated with PICH were included for consideration in the multivariate model.…”

Section: Statistical Analysis Model Development and Model Assessmentmentioning

confidence: 99%

Incorporating Immunoproteins in the Development of Classification Models of Progression of Intracranial Hemorrhage After Traumatic Brain Injury

Hinson

Myers

et al. 2021

Journal of Head Trauma Rehabilitation

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Objective: To define clinical, radiographic, and blood-based biomarker features to be incorporated into a classification model of progression of intracranial hemorrhage (PICH), and to provide a pilot assessment of those models. Methods: Patients with hemorrhage on admission head computed tomography were identified from a prospectively enrolled cohort of subjects with traumatic brain injury. Initial and follow-up images were interpreted both by 2 independent readers, and disagreements adjudicated. Admission plasma samples were analyzed and principal components (PCs) composed of the immune proteins (IPs) significantly associated with the outcome of interest were selected for further evaluation. A series of logistic regression models were constructed based on (1) clinical variables (CV) and ( 2) clinical variables + immune proteins (CV+IP). Error rates of these models for correct classification of PICH were estimated; significance was set at P < .05. Results: We identified 106 patients, 36% had PICH. Dichotomized admission Glasgow Coma Scale (P = .004), Marshall score (P = .004), and 3 PCs were significantly associated with PICH. For the CV only model, sensitivity was 1.0 and specificity was 0.29 (95% CI, 0.07-0.67). The CV+IP model performed significantly better, with a sensitivity of 0.93 (95% CI, 0.64-0.99) and a specificity of 1.0 (P = .008). Adjustments to refine the definition of PICH and better define radiographic predictors of PICH did not significantly improve the models' performance. Conclusions: In this pilot investigation, we observed that composites of IPs may improve PICH classification models when combined with CVs. However, overall model performance must be further optimized; results will inform feature inclusion included in follow-up models.

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“…Leptin levels in peripheral blood are highly associated with cerebral hemorrhagic or ischemic stroke (Soderberg et al 1999; Soderberg et al 2003) and independently predict in hospital and 1-week mortality rates of patients with ICH, as well as 6-month clinical outcomes in pediatric traumatic brain injury (Dong et al 2010; Lin et al 2012; Zhao et al 2012). Higher plasma leptin levels correlate with disease severity and markers of systemic inflammation and thus represent a novel biomarker for predicting 6-month clinical outcomes in patients with ICH (Zhao et al 2012).…”

Section: Systemic Responses Post-ichmentioning

confidence: 99%

Progress in translational research on intracerebral hemorrhage: Is there an end in sight?

Strahle

Hua

et al. 2014

Progress in Neurobiology

130

107

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Intracerebral hemorrhage (ICH) is a common and often fatal stroke subtype for which specific therapies and treatments remain elusive. To address this, many recent experimental and translational studies of ICH have been conducted, and these have led to several ongoing clinical trials. This review focuses on the progress of translational studies of ICH including those of the underlying causes and natural history of ICH, animal models of the condition, and effects of ICH on the immune and cardiac systems, among others. Current and potential clinical trials also are discussed for both ICH alone and with intraventricular extension.

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Relationship between plasma leptin levels and clinical outcomes of pediatric traumatic brain injury

Cited by 15 publications

References 35 publications

Serum Neutrophil Gelatinase–Associated Lipocalin Predicts Survival After Resuscitation From Cardiac Arrest

Serum Neutrophil Gelatinase–Associated Lipocalin Predicts Survival After Resuscitation From Cardiac Arrest

Incorporating Immunoproteins in the Development of Classification Models of Progression of Intracranial Hemorrhage After Traumatic Brain Injury

Progress in translational research on intracerebral hemorrhage: Is there an end in sight?

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