Biomarker Insights最新文献

Environmental and occupational exposure to heavy metals and metalloids is a major global health risk. The kidney is often a site of early damage. Nephrotoxicity is both a major consequence of heavy metal exposure and potentially an early warning of greater damage. A paradigm shift occurred at the beginning of the 21st century in the field of renal medicine. The medical model of kidney failure and treatment began to give way to a social model of risk factors and prevention with important implications for environmental health. This development threw into focus the need for better biomarkers: markers of exposure to known nephrotoxins; markers of early damage for diagnosis and prevention; markers of disease development for intervention and choice of therapy. Constituents of electronic waste, e-waste or e-pollution, such as cadmium (Cd), lead (Pb), mercury (HG), arsenic (As) and silica (SiO₂) are all potential nephrotoxins; they target the renal proximal tubules through distinct pathways. Different nephrotoxic biomarkers offer the possibility of identifying exposure to individual pollutants. In this review, a selection of prominent urinary markers of tubule damage is considered as potential tools for identifying environmental exposure to some key metallic pollutants.

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.

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.

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.