Biomarker Insights最新文献

Introduction: Predicting disease severity is important for treatment decisions in patients with COVID-19 in the intensive care unit (ICU). Different biomarkers have been investigated in COVID-19 as predictor of mortality, including C-reactive protein (CRP), procalcitonin (PCT), interleukin-6 (IL-6), and soluble urokinase-type plasminogen activator receptor (suPAR). Using repeated measurements in a prediction model may result in a more accurate risk prediction than the use of single point measurements. The goal of this study is to investigate the predictive value of trends in repeated measurements of CRP, PCT, IL-6, and suPAR on mortality in patients admitted to the ICU with COVID-19.

Methods: This was a retrospective single center cohort study. Patients were included if they tested positive for SARS-CoV-2 by PCR test and if IL-6, PCT, suPAR was measured during any of the ICU admission days. There were no exclusion criteria for this study. We used joint models to predict ICU-mortality. This analysis was done using the framework of joint models for longitudinal and survival data. The reported hazard ratios express the relative change in the risk of death resulting from a doubling or 20% increase of the biomarker's value in a day compared to no change in the same period.

Results: A total of 107 patients were included, of which 26 died during ICU admission. Adjusted for sex and age, a doubling in the next day in either levels of PCT, IL-6, and suPAR were significantly predictive of in-hospital mortality with HRs of 1.523 (1.012-6.540), 75.25 (1.116-6247), and 24.45 (1.696-1057) respectively. With a 20% increase in biomarker value in a subsequent day, the HR of PCT, IL-6, and suPAR were 1.117 (1.03-1.639), 3.116 (1.029-9.963), and 2.319 (1.149-6.243) respectively.

Conclusion: Joint models for the analysis of repeated measurements of PCT, suPAR, and IL-6 are a useful method for predicting mortality in COVID-19 patients in the ICU. Patients with an increasing trend of biomarker levels in consecutive days are at increased risk for mortality.

The following article aims to review COVID-19 biomarkers used in hospital practice. It is apparent that COVID-19 is not simply a pulmonary disease but has systemic manifestations. For this reason, biomarkers must be used in the management of diagnosed patients to provide holistic care. Patients with COVID-19 have been shown to have pulmonary, hepatobiliary, cardiovascular, neurologic, and renal injury, along with coagulopathy and a distinct cytokine storm. Biomarkers can effectively inform clinicians of systemic organ injury due to COVID-19. Furthermore, biomarkers can be used in predictive models for severe COVID-19 in admitted patients. The utility of doing so is to allow for risk stratification and utilization of proper treatment protocols. In addition, COVID-19 biomarkers in the pediatric population are discussed, specifically in predicting Multisystem Inflammatory Syndrome. Ultimately, biomarkers can be used as predictive tools to allow clinicians to identify and adequately manage patients at increased risk for worse outcomes from COVID-19. Both literature review and anecdotal evidence has shown that severe COVID-19 is a systemic disease, and understanding associated biomarkers are crucial for hospitalized patients' proper clinical decision-making. For example, the cytokine storm releases inflammatory markers in different organ systems such as the pulmonary, hepatobiliary, hematological, cardiac, neurological, and renal systems. This review summarizes the latest research of COVID-19 that can help inform healthcare professionals how to better mitigate morbidity and mortality associated with this disease and provides information about certain systemic biomarkers that can be incorporated into hospital practice to provide more comprehensive care for hospitalized COIVD-19 patients.

Introduction: CLN3 Batten disease is a rare pediatric neurodegenerative lysosomal disorder caused by biallelic disease-associated variants in CLN3. Despite decades of intense research, specific biofluid biomarkers of disease status have not been reported, hindering clinical development of therapies. Thus, we sought to determine whether individuals with CLN3 Batten disease have elevated levels of specific metabolites in blood.

Methods: We performed an exhaustive metabolomic screen using serum samples from a novel minipig model of CLN3 Batten disease and validated findings in CLN3 pig serum and CSF and Cln3 mouse serum. We further validate biomarker candidates with a retrospective analysis of plasma and CSF samples collected from participants in a natural history study. Plasma samples were evaluated from 22 phenotyped individuals with CLN3 disease, 15 heterozygous carriers, and 6 non-affected non-carriers (NANC).

Results: CLN3 pig serum samples from 4 ages exhibited large elevations in 4 glycerophosphodiester species: glycerophosphoinositol (GPI), glycerophosphoethanolamine (GPE), glycerophosphocholine (GPC), and glycerophosphoserine (GPS). GPI and GPE exhibited the largest elevations, with similar elevations found in CLN3 pig CSF and Cln3 mouse serum. In plasma samples from individuals with CLN3 disease, glycerophosphoethanolamine and glycerophosphoinositol were significantly elevated with glycerophosphoinositol exhibiting the clearest separation (mean 0.1338 vs 0.04401 nmol/mL for non-affected non-carriers). Glycerophosphoinositol demonstrated excellent sensitivity and specificity as a biomarker, with a receiver operating characteristic area under the curve of 0.9848 (P = .0003).

Conclusions: GPE and GPI could have utility as biomarkers of CLN3 disease status. GPI, in particular, shows consistent elevations across a diverse cohort of individuals with CLN3. This raises the potential to use these biomarkers as a blood-based diagnostic test or as an efficacy measure for disease-modifying therapies.

Objectives: Disruption in the natural immune reaction due to SARS-CoV-2 infection can initiate a potent cytokine storm among COVID-19 patients. An elevated level of IL-6 and IL-10 during a hyperinflammatory state plays a vital role in increasing the risk of severity and mortality. In this study, we aimed to evaluate the potential of circulating IL-6 and IL-10 levels as biomarkers for detecting the severity and mortality of COVID-19.

Methods: This study was conducted according to the Cochrane Handbook and PRISMA guidelines. Authorized databases were searched to extract suitable studies using specific search terms. RevMan 5.4 was applied for performing the meta-analysis. Mean differences in IL-6 and IL-10 levels were calculated among COVID-19 patients via a random-effects model. NOS scoring, publication bias and sensitivity analyses were checked to ensure study quality.

Results: A total of 147 studies were selected, with 31 909 COVID-19 patients under investigation. In the severity analysis, the mean concentration of IL-6 was significantly higher in the severe COVID-19 cases than in the non-severe cases (MD: 19.98; P < .001; 95% CI: 17.56, 22.40). Similar result was observed for IL-10 mean concentration in severe COVID-19 cases (MD: 1.35; P < .001; 95% CI: 0.90, 1.80). In terms of mortality analysis, circulating IL-6 showed sharp elevation in the deceased patients (MD: 42.11; P < .001; 95% CI: 36.86, 47.36). IL-10 mean concentration was higher in the dead patients than in the survived patients (MD: 4.79; P < .001; 95% CI: 2.83, 6.75). Publication bias was not found except for comparing IL-6 levels with disease severity. Sensitivity analysis also reported no significant deviation from the pooled outcomes.

Conclusions: Elevated levels of circulating IL-6 and IL-10 signifies worsening of COVID-19. To monitor the progression of SARS-CoV-2 infection, IL-6 and IL-10 should be considered as potential biomarkers for severity and mortality detection in COVID-19.

Systematic review registration: INPLASY registration number: INPLASY202240046.

Traumatic brain injury (TBI) is not a single disease state but describes an array of conditions associated with insult or injury to the brain. While some individuals with TBI recover within a few days or months, others present with persistent symptoms that can cause disability, neuropsychological trauma, and even death. Understanding, diagnosing, and treating TBI is extremely complex for many reasons, including the variable biomechanics of head impact, differences in severity and location of injury, and individual patient characteristics. Because of these confounding factors, the development of reliable diagnostics and targeted treatments for brain injury remains elusive. We argue that the development of effective diagnostic and therapeutic strategies for TBI requires a deep understanding of human neurophysiology at the molecular level and that the framework of multiomics may provide some effective solutions for the diagnosis and treatment of this challenging condition. To this end, we present here a comprehensive review of TBI biomarker candidates from across the multiomic disciplines and compare them with known signatures associated with other neuropsychological conditions, including Alzheimer's disease and Parkinson's disease. We believe that this integrated view will facilitate a deeper understanding of the pathophysiology of TBI and its potential links to other neurological diseases.

Introduction: An early diagnosis is crucial in reducing mortality among people who have breast cancer (BC). There is a shortfall of characteristic early clinical symptoms in BC patients, highlighting the importance of investigating new methods for its early detection. A promising novel approach is the analysis of volatile organic compounds (VOCs) produced and emitted through the metabolism of cancer cells.

Methods: The purpose of this systematic review is to outline the published research regarding BC-associated VOCs. For this, headspace analysis of VOCs was explored in patient-derived body fluids, animal model-derived fluids, and BC cell lines to identify BC-specific VOCs. A systematic search in PubMed and Web of Science databases was conducted according to the PRISMA guidelines.

Results: Thirty-two studies met the criteria for inclusion in this review. Results highlight that VOC analysis can be promising as a potential novel screening tool. However, results of in vivo, in vitro and case-control studies have delivered inconsistent results leading to a lack of inter-matrix consensus between different VOC sampling methods.

Discussion: Discrepant VOC results among BC studies have been obtained, highly due to methodological discrepancies. Therefore, methodological issues leading to disparities have been reviewed and recommendations have been made on the standardisation of VOC collection and analysis methods for BC screening, thereby improving future VOC clinical validation studies.

Background: Methylation at 5 CpG sites was previously shown to classify chronic lymphocytic leukemia (CLL) into 3 prognostic subgroups. Here, we aimed to validate the marker set in an additional cohort and to evaluate its clinical utility for CLL patient stratification.

Methods: We evaluated this epigenetic marker set in 79 German patients using bisulfite treatment followed by pyrosequencing and classification using a support vector machine-learning tool.

Results: The n-CLL, i-CLL, and m-CLL classification was detected in 28 (35%), 10 (13%), and 41 (51%) patients, respectively. Epigenetic grouping was associated with IGHV mutational status (P = 2 × 10^-12), isolated del13q (P = 9 × 10^-6), del17p (P = .015), complex karyotype (P = .005), VH-usage, and clinical outcome as time to first treatment (P = 1.4 × 10^-12) and overall survival (P = .003). Multivariate Cox regression analysis identified n-CLL as a factor for earlier treatment hazard ratio (HR), 6.3 (95% confidence interval [CI] 2.4-16.4; P = .0002) compared to IGHV mutational status (HR 4.6, 95% CI 1.9-11.3, P = .0008). In addition, when comparing the prognostic value of the epigenetic classification system with the IGHV classification, epigenetic grouping performed better compared to IGHV mutational status using Kaplan-Meier estimation and allowed the identification of a third, intermediate (i-CLL) group. Thus, our study confirmed the prognostic value of the epigenetic marker set for patient stratification in routine clinical diagnostics.

Next-generation sequencing-based genomic profiling facilitates biomarker detection by cell-free DNA (cfDNA) liquid biopsy. However, the efficiency of mutation calling and the prognostic value of cfDNA biomarkers are disputed. We investigated 24 patients with gastric cancer in this study, using a 605-gene sequencing panel to sequence their plasma cfDNA and tumor tissue DNA. The mutation concordance between plasma cfDNA and tumor tissue DNA was 70.6% in stage IV gastric cancer and 30.2% in stage III gastric cancer, indicating insufficient mutation detection rates in stage III and early-stage cancer. When compared with total cfDNA load and blood tumor mutation burden (bTMB), the variant allele frequencies (VAF) of commonly mutated genes are highly accurate in representing disease burden. Further, VAF are a better prognostic indicator compared with serum biomarkers including carcinoembryonic antigen (CEA), carbohydrate antigen 19-9 (CA19-9), cancer antigen 125 (CA125), and alpha-fetoprotein (AFP). The use of cfDNA in molecular profiling of patients allows prediction of patient survival and clinical response, as well as the development of personalized therapy regimens.