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Clinical Chemistry Jan 2017Neprilysin (NEP) is a membrane-bound neutral endopeptidase that degrades a variety of bioactive peptides. The substrates include natriuretic peptides (NPs), which are... (Review)
Review
BACKGROUND
Neprilysin (NEP) is a membrane-bound neutral endopeptidase that degrades a variety of bioactive peptides. The substrates include natriuretic peptides (NPs), which are important regulating mediators for cardiovascular and renal biology. Inhibition of NEP activity and exogenous NP administration thus have emerged as potential therapeutic strategies for treating cardiorenal diseases. More recently, B-type natriuretic peptide (BNP) or N-terminal-proBNP (NT-proBNP), 3'-5' cyclic guanosine monophosphate (cGMP), and soluble NEP as biomarkers have also been investigated in heart failure (HF) trials and their predictive value are beginning to be recognized.
CONTENT
The biological functions of NEP and NPs are discussed. Enhancing NPs through NEP inhibition combined with renin-angiotensin-aldosterone system (RAAS) antagonism has proved to be successful in HF treatment, although future surveillance studies will be required. Direct NP enhancement through peptide delivery may have fewer potentially hazardous effects compared to NEP inhibition. Strategies of combined inhibition on NEP with other cardiorenal pathophysiological pathways are promising. Finally, monitoring BNP/NT-proBNP/cGMP concentrations during NEP inhibition treatment may provide supplemental benefits to conventional biomarkers, and the identification of soluble NEP as a novel biomarker for HF needs further investigation.
SUMMARY
In this review, the biology of NEP is summarized, with a focus on NP regulation. The degradation of NPs by NEP provides the rationale for NEP inhibition as a strategy for cardiorenal disease treatment. We also describe the current therapeutic strategies of NEP inhibition and NP therapeutics in cardiorenal diseases. Moreover, the discovery of its circulating form, soluble NEP, as a biomarker is also discussed.
Topics: Animals; Biomarkers; Cardio-Renal Syndrome; Humans; Natriuretic Peptides; Neprilysin
PubMed: 28062615
DOI: 10.1373/clinchem.2016.262907 -
Osteoarthritis and Cartilage Feb 2017The aim of this "Year in Review" article is to summarize and discuss the implications of biochemical marker related articles published between the Osteoarthritis... (Review)
Review
PURPOSE
The aim of this "Year in Review" article is to summarize and discuss the implications of biochemical marker related articles published between the Osteoarthritis Research Society International (OARSI) 2015 Congress in Seattle and the OARSI 2016 Congress in Amsterdam.
METHODS
The PubMed/MEDLINE bibliographic database was searched using the combined keywords: 'biomarker' and 'osteoarthritis'. The PubMed/MEDLINE literature search was conducted using the Advanced Search Builder function (http://www.ncbi.nlm.nih.gov/pubmed/advanced).
RESULTS
Over two hundred new biomarker-related papers were published during the literature search period. Some papers identified new biomarkers whereas others explored the biological properties and clinical utility of existing markers. There were specific references to several adipocytokines including leptin and adiponectin. ADAM Metallopeptidase with Thrombospondin Type 1 motif 4 (ADAMTS-4) and aggrecan ARGS neo-epitope fragment (ARGS) in synovial fluid (SF) and plasma chemokine (CeC motif) ligand 3 (CCL3) were reported as potential new knee biomarkers. New and refined proteomic technologies and novel assays including a fluoro-microbead guiding chip (FMGC) for measuring C-telopeptide of type II collagen (CTX-II) in serum and urine and a novel magnetic nanoparticle-based technology (termed magnetic capture) for collecting and concentrating CTX-II, were described this past year.
CONCLUSION
There has been steady progress in osteoarthritis (OA) biomarker research in 2016. Several novel biomarkers were identified and new technologies have been developed for measuring existing biomarkers. However, there has been no "quantum leap" this past year and identification of novel early OA biomarkers remains challenging. During the past year, OARSI published a set of recommendations for the use of soluble biomarkers in clinical trials, which is a major step forward in the clinical use of OA biomarkers and bodes well for future OA biomarker development.
Topics: Animals; Biomarkers; Humans; Magnetite Nanoparticles; Osteoarthritis; Proteomics
PubMed: 28099838
DOI: 10.1016/j.joca.2016.12.016 -
PloS One 2023This study was designed to investigate the relationship between a systematic inflammatory biomarker measure, concurrent and later cognitive performance, and future...
This study was designed to investigate the relationship between a systematic inflammatory biomarker measure, concurrent and later cognitive performance, and future dementia risk. The literature has reported the potential involvement of inflammation in cognitive performance as well as Alzheimer's Disease, but not consistently. We used a population-based cohort of 500,000 people in the UK and assessed the association between a composite inflammatory biomarker and cognitive performance measures across five domains measured concurrently and 4-13 years later, taking advantage of the large sample size. We also assessed the same biomarker's association with dementia diagnosis 3-11 years later in the initially dementia-free sample. We report small but significant associations between elevated biomarker levels and worsened cognitive performance at baseline for four cognitive tasks (OR = 1.204, p<0.001 for Prospective memory, β = -0.366, p<0.001 for Fluid intelligence, β = 8.819, p<0.001 for Reaction time, and β = -0.224, p<0.001 for Numeric memory), comparing the highest quartile of the biomarker to the lowest. We also found that for one measure (Pairs matching) higher biomarker levels were associated with fewer errors, i.e. better performance (β = -0.096, p<0.001). We also report that the 4th quartiles of the baseline biomarker levels were significantly associated with cognitive task scores assessed years later on the p< = 0.002 level, except for the Pair matching test, for which none of the quartiles remained a significant predictor. Finally, the highest biomarker quartile was significantly associated with increased dementia risk compared to the lowest quartile (HR = 1.349, p<0.001). A case-only analysis to assess disease subtype heterogeneity suggested probable differences in the association with the highest biomarker quartile between vascular dementia and Alzheimer disease subtypes (OR = 1.483, p = 0.055). Our results indicate that systemic inflammation may play a small but significant part in dementia pathophysiology, especially in vascular dementia.
Topics: Humans; Dementia, Vascular; Biological Specimen Banks; Alzheimer Disease; Biomarkers; Inflammation; United Kingdom; Cognitive Dysfunction
PubMed: 37467176
DOI: 10.1371/journal.pone.0288045 -
Osteoarthritis and Cartilage Nov 2023To highlight the advances over the past year in metabolite/protein biomarkers for osteoarthritis (OA). (Review)
Review
OBJECTIVE
To highlight the advances over the past year in metabolite/protein biomarkers for osteoarthritis (OA).
METHOD
A literature search of five databases including PubMed, Web of Science, Scopus, Ovid Medline, and Embase was performed for studies on metabolite/protein/peptide/biochemical markers for OA published between April 1st, 2022 and March 31st, 2023. Records were then screened to include only original research articles using directly collected human specimens, in English language, and with full text available. Data from eligible studies were systematically extracted and summarized.
RESULTS
A total of 1600 unique records were extracted, out of which 46 fulfilled the inclusion criteria and were used for data extraction. Forty-one of these 46 studies focused on biomarkers for OA/OA severity/progression, four on OA clustering, and one on OA treatment outcomes. Twenty-nine studied protein markers for OA, thirteen studied metabolite markers, and four studied both. While many studies were the validation of the previously reported biomarkers, a number of novel metabolite/protein biomarkers and biomarker panels were reported in the past year. Biomarker panels might be useful to subset OA patients.
CONCLUSION
The number of studies on OA clustering is rising. Although validation in larger cohorts is needed in order to utilize reported biomarkers in clinical practice, these discoveries help better understand the pathogenesis of OA, provide insights into possible mechanisms underlying poor treatment outcomes, and aid in developing personalized treatment based on OA subtypes.
Topics: Humans; Osteoarthritis; Biomarkers; Treatment Outcome
PubMed: 37611797
DOI: 10.1016/j.joca.2023.08.005 -
Genes Feb 2023Networks-based approaches are often used to analyze gene expression data or protein-protein interactions but are not usually applied to study the relationships between... (Review)
Review
Networks-based approaches are often used to analyze gene expression data or protein-protein interactions but are not usually applied to study the relationships between different biomarkers. Given the clinical need for more comprehensive and integrative biomarkers that can help to identify personalized therapies, the integration of biomarkers of different natures is an emerging trend in the literature. Network analysis can be used to analyze the relationships between different features of a disease; nodes can be disease-related phenotypes, gene expression, mutational events, protein quantification, imaging-derived features and more. Since different biomarkers can exert causal effects between them, describing such interrelationships can be used to better understand the underlying mechanisms of complex diseases. Networks as biomarkers are not yet commonly used, despite being proven to lead to interesting results. Here, we discuss in which ways they have been used to provide novel insights into disease susceptibility, disease development and severity.
Topics: Humans; Biomarkers; Disease Susceptibility; Phenotype; Proteins
PubMed: 36833356
DOI: 10.3390/genes14020429 -
Journal of Nuclear Medicine : Official... May 2021Discovery of biomarkers has been steadily increasing over the past decade. Although a plethora of biomarkers has been reported in the biomedical literature, few have... (Review)
Review
Discovery of biomarkers has been steadily increasing over the past decade. Although a plethora of biomarkers has been reported in the biomedical literature, few have been sufficiently validated for broader clinical applications. One particular challenge that may have hindered the adoption of biomarkers into practice is the lack of reproducible biomarker cut points. In this article, we attempt to identify some common statistical issues related to biomarker cut point identification and provide guidance on proper evaluation, interpretation, and validation of such cut points. First, we illustrate how discretization of a continuous biomarker using sample percentiles results in significant information loss and should be avoided. Second, we review the popular "minimal--value" approach for cut point identification and show that this method results in highly unstable values and unduly increases the chance of significant findings when the biomarker is not associated with outcome. Third, we critically review a common analysis strategy by which the selected biomarker cut point is used to categorize patients into different risk categories and then the difference in survival curves among these risk groups in the same dataset is claimed as the evidence supporting the biomarker's prognostic strength. We show that this method yields an exaggerated value and overestimates the prognostic impact of the biomarker. We illustrate that the degree of the optimistic bias increases with the number of variables being considered in a risk model. Finally, we discuss methods to appropriately ascertain the additional prognostic contribution of the new biomarker in disease settings where standard prognostic factors already exist. Throughout the article, we use real examples in oncology to highlight relevant methodologic issues, and when appropriate, we use simulations to illustrate more abstract statistical concepts.
Topics: Biomarkers; Data Interpretation, Statistical; Humans; Statistics as Topic
PubMed: 33579807
DOI: 10.2967/jnumed.120.251520 -
Clinical Chemistry Jan 2012Metabolomics, the systematic analysis of low molecular weight biochemical compounds in a biological specimen, has been increasingly applied to biomarker discovery. (Review)
Review
BACKGROUND
Metabolomics, the systematic analysis of low molecular weight biochemical compounds in a biological specimen, has been increasingly applied to biomarker discovery.
CONTENT
Because no single analytical method can accommodate the chemical diversity of the entire metabolome, various methods such as nuclear magnetic resonance spectroscopy (NMR) and mass spectrometry (MS) have been employed, with the latter coupled to an array of separation techniques including gas and liquid chromatography. Whereas NMR can provide structural information and absolute quantification for select metabolites without the use of exogenous standards, MS tends to have much higher analytical sensitivity, enabling broader surveys of the metabolome. Both NMR and MS can be used to characterize metabolite data either in a targeted manner or in a nontargeted, pattern-recognition manner. In addition to technical considerations, careful sample selection and study design are important to minimize potential confounding influences on the metabolome, including diet, medications, and comorbitidies. To this end, metabolite profiling has been applied to human biomarker discovery in small-scale interventions, in which individuals are extremely well phenotyped and able to serve as their own biological controls, as well as in larger epidemiological cohorts. Understanding how metabolites relate to each other and to established risk markers for diseases such as diabetes and renal failure will be important in evaluating the potential value of these metabolites as clinically useful biomarkers.
SUMMARY
Applied to both experimental and epidemiological study designs, metabolite profiling has begun to highlight the breadth metabolic disturbances that accompany human disease. Experimental work in model systems and integration with other functional genomic approaches will be required to establish a causal link between select biomarkers and disease pathogenesis.
Topics: Biomarkers; Cardiovascular Diseases; Chromatography, Gas; Chromatography, Liquid; Epidemiologic Methods; Humans; Magnetic Resonance Spectroscopy; Mass Spectrometry; Metabolome
PubMed: 22110018
DOI: 10.1373/clinchem.2011.169573 -
Osteoarthritis and Cartilage Mar 2019The aim of this narrative review is to summarize important findings from biochemical marker studies relevant to osteoarthritis (OA) in the context of new discoveries and... (Review)
Review
OBJECTIVE
The aim of this narrative review is to summarize important findings from biochemical marker studies relevant to osteoarthritis (OA) in the context of new discoveries and clinical and scientific need.
DESIGN
We conducted a systematic search of electronic medical databases (Embase, Medline, Web of Science, Cochrane central) between 01-03-2017 and 31-03-2018. The search was restricted to human studies, English language and full text available publications while reviews were excluded. Only papers describing protein based biomarkers measured in human body fluids (blood, urine and synovial fluid (SF)) were included. Of the 992 papers, 86 were reviewed here, with inclusion primarily based on relevance to OA biochemical markers.
RESULTS
This review highlights a selection of studies based on their quality and perceived importance to the field mainly including those that evaluate prognostic value of biomarkers for OA progression (i.e., biomarkers reflecting change in composition of joint tissues and biomarkers of inflammation), help in assessment of intervention efficacy, and are innovative and uncover new candidate biomarkers, or use new approaches in biomarker discovery.
CONCLUSIONS
Key findings and implications for possible clinical utility of biochemical markers are summarized and discussed. Given the paucity of robust biomarkers within the field, and the heterogeneity of the condition, enormous works are needed for development and validation of novel and clinically applicable biomarkers to reduce the impact of this highly prevalent and debilitating condition.
Topics: Biomarkers; Disease Progression; Humans; Osteoarthritis
PubMed: 30552966
DOI: 10.1016/j.joca.2018.12.002 -
The Journal of Biological Chemistry Feb 2022Plasma and urine glycosaminoglycans (GAGs) are long, linear sulfated polysaccharides that have been proposed as potential noninvasive biomarkers for several diseases....
Plasma and urine glycosaminoglycans (GAGs) are long, linear sulfated polysaccharides that have been proposed as potential noninvasive biomarkers for several diseases. However, owing to the analytical complexity associated with the measurement of GAG concentration and disaccharide composition (the so-called GAGome), a reference study of the normal healthy GAGome is currently missing. Here, we prospectively enrolled 308 healthy adults and analyzed their free GAGomes in urine and plasma using a standardized ultra-high-performance liquid chromatography coupled with triple-quadrupole tandem mass spectrometry method together with comprehensive demographic and blood chemistry biomarker data. Of 25 blood chemistry biomarkers, we mainly observed weak correlations between the free GAGome and creatinine in urine and hemoglobin or erythrocyte counts in plasma. We found a higher free GAGome concentration - but not a more diverse composition - in males. Partitioned by gender, we also established reference intervals for all detectable free GAGome features in urine and plasma. Finally, we carried out a transference analysis in healthy individuals from two distinct geographical sites, including data from the Lifelines Cohort Study, which validated the reference intervals in urine. Our study is the first large-scale determination of normal free GAGomes reference intervals in plasma and urine and represents a critical resource for future physiology and biomarker research.
Topics: Adult; Biomarkers; Chromatography, High Pressure Liquid; Cohort Studies; Glycosaminoglycans; Humans; Male; Tandem Mass Spectrometry
PubMed: 35007531
DOI: 10.1016/j.jbc.2022.101575 -
Brain : a Journal of Neurology Dec 2023The recent validation of the α-synuclein seed amplification assay as a biomarker with high sensitivity and specificity for the diagnosis of Parkinson's disease has...
The recent validation of the α-synuclein seed amplification assay as a biomarker with high sensitivity and specificity for the diagnosis of Parkinson's disease has formed the backbone for a proposed staging system for incorporation in Parkinson's disease clinical studies and trials. The routine use of this biomarker should greatly aid in the accuracy of diagnosis during recruitment of Parkinson's disease patients into trials (as distinct from patients with non-Parkinson's disease parkinsonism or non-Parkinson's disease tremors). There remain, however, further challenges in the pursuit of biomarkers for clinical trials of disease modifying agents in Parkinson's disease, namely: optimizing the distinction between different α-synucleinopathies; the selection of subgroups most likely to benefit from a candidate disease modifying agent; a sensitive means of confirming target engagement; and the early prediction of longer-term clinical benefit. For example, levels of CSF proteins such as the lysosomal enzyme β-glucocerebrosidase may assist in prognostication or allow enrichment of appropriate patients into disease modifying trials of agents with this enzyme as the target; the presence of coexisting Alzheimer's disease-like pathology (detectable through CSF levels of amyloid-β42 and tau) can predict subsequent cognitive decline; imaging techniques such as free-water or neuromelanin MRI may objectively track decline in Parkinson's disease even in its later stages. The exploitation of additional biomarkers to the α-synuclein seed amplification assay will, therefore, greatly add to our ability to plan trials and assess the disease modifying properties of interventions. The choice of which biomarker(s) to use in the context of disease modifying clinical trials will depend on the intervention, the stage (at risk, premotor, motor, complex) of the population recruited and the aims of the trial. The progress already made lends hope that panels of fluid biomarkers in tandem with structural or functional imaging may provide sensitive and objective methods of confirming that an intervention is modifying a key pathophysiological process of Parkinson's disease. However, correlation with clinical progression does not necessarily equate to causation, and the ongoing validation of quantitative biomarkers will depend on insightful clinical-genetic-pathophysiological comparisons incorporating longitudinal biomarker changes from those at genetic risk with evidence of onset of the pathophysiology and those at each stage of manifest clinical Parkinson's disease.
Topics: Humans; Parkinson Disease; alpha-Synuclein; Biomarkers; Cognitive Dysfunction; Longitudinal Studies
PubMed: 37536279
DOI: 10.1093/brain/awad265