Review

Authors: P Kunc, J Fabry, M Lucanska...

Asthma is a complex disease with a variable course. Efforts to identify biomarkers to predict asthma severity, the course of disease and response to treatment have not been very successful so far. Biomarker research has expanded greatly with the advancement of molecular research techniques. An ideal biomarker should be suitable to identify the disease as well the specific endotype/phenotype, useful in the monitoring of the disease and to determine the prognosis, easily to obtain with minimum discomfort or risk to the patient. An ideal biomarker should be suitable to identify the disease as well the specific endotype/phenotype, useful in the monitoring of the disease and to determine the prognosis, easily to obtain with minimum discomfort or risk to the patient - exhaled breath analysis, blood cells and serum biomarkers, sputum cells and mediators and urine metabolites could be potential biomarkers of asthma bronchiale. Unfortunately, at the moment, an ideal biomarker doesn't exist and the overlap between the biomarkers is a reality. Using panels of biomarkers could improve probably the identification of asthma endotypes in the era of precision medicine.

Topics: Animals; Asthma; Biomarkers; Humans; Precision Medicine; Predictive Value of Tests; Sputum

PubMed: 32228009
DOI: 10.33549/physiolres.934398

Review

Authors: Brock E Boehm, Monica E York, Gyorgy Petrovics...

In the United States, prostate cancer (CaP) remains the second leading cause of cancer deaths in men. CaP is predominantly indolent at diagnosis, with a small fraction (25-30%) representing an aggressive subtype (Gleason score 7-10) that is prone to metastatic progression. This fact, coupled with the criticism surrounding the role of prostate specific antigen in prostate cancer screening, demonstrates the current need for a biomarker(s) that can identify clinically significant CaP and avoid unnecessary biopsy procedures and psychological implications of being diagnosed with low-risk prostate cancer. Although several diagnostic biomarkers are available to clinicians, very few comparative trials have been performed to assess the clinical effectiveness of these biomarkers. It is of note, however, that a majority of these clinical trials have been over-represented by men of Caucasian origin, despite the fact that African American men have a 1.7 times higher incidence and 2.1 times higher rate of mortality from prostate cancer. Biomarkers for CaP diagnosis based on the tissue of origin include urine-based gene expression assays (PCA3, Select MDx, ExoDx Prostate IntelliScore, Mi-Prostate Score, PCA3-PCGEM1 gene panel), blood-based protein biomarkers (4K, PHI), and tissue-based DNA biomarker (Confirm MDx). Another potential direction that has emerged to aid in the CaP diagnosis include multi-parametric magnetic resonance imaging (mpMRI) and bi-parametric magnetic resonance imaging (bpMRI), which in conjunction with clinically validated biomarkers may provide a better approach to predict clinically significant CaP at diagnosis. In this review, we discuss some of the adjunctive biomarker tests along with newer imaging modalities that are currently available to help clinicians decide which patients are at risk of having high-grade CaP on prostate biopsy with the emphasis on clinical utility of the tests across African American (AA) and Caucasian (CA) men.

Topics: Male; Humans; United States; Prostatic Neoplasms; Prostate; Prostate-Specific Antigen; Early Detection of Cancer; Biopsy; Biomarkers, Tumor

PubMed: 36768533
DOI: 10.3390/ijms24032185

Review

Authors: Yusheng Liu, Hua Wang

Cancer stem cells (CSCs) are a small subpopulation of cancer cells with capabilities of self-renewal, differentiation, and tumorigenicity, and play a critical role in driving tumor heterogeneity that evolves insensitivity to therapeutics. For these reasons, extensive efforts have been made to identify and target CSCs to potentially improve the antitumor efficacy of therapeutics. While progress has been made to uncover certain CSC-associated biomarkers, the identification of CSC-specific markers, especially the targetable ones, remains a significant challenge. Here we provide an overview of the unique signaling and metabolic pathways of CSCs, summarize existing CSC biomarkers and CSC-targeted therapies, and discuss strategies to further differentiate CSCs from non-stem cancer cells and healthy cells for the development of enhanced CSC-targeted therapies.

Topics: Humans; Neoplasms; Biomarkers; Neoplastic Stem Cells; Signal Transduction; Cell Differentiation; Biomarkers, Tumor

PubMed: 38071088
DOI: 10.1016/j.tips.2023.11.006

Review

Authors: Anzari Atik, Tessandra Stewart, Jing Zhang...

Parkinson's disease (PD) is a common neurodegenerative disorder, characterized pathologically by the presence of α-synuclein (α-syn)-rich Lewy bodies. As clinical diagnosis of PD is challenging, misdiagnosis is common, highlighting the need for disease-specific and early stage biomarkers. Both early diagnosis of PD and adequate tracking of disease progression could significantly improve outcomes for patients, particularly in regard to existing and future disease modifying treatments. Given its critical roles in PD pathogenesis, α-syn may be useful as a biomarker of PD. The aim of this review is, therefore, to summarize the efficacy of tissue and body fluid α-syn measurements in the detection of PD as well as monitoring disease progression. In comparison to solid tissue specimens and biopsies, biofluid α-syn levels may be the most promising candidates in PD diagnosis and progression based on specificity, sensitivity and availability. Although α-syn has been tested most extensively in cerebrospinal fluid (CSF), the relatively invasive procedure for collecting CSF is not suitable in most clinical settings, leading to investigation of plasma, blood and saliva as alternatives. The exploration of combined biomarkers, along with α-syn, to improve diagnostic accuracy is also likely required.

Topics: Biomarkers; Humans; Parkinson Disease; alpha-Synuclein

PubMed: 26940058
DOI: 10.1111/bpa.12370

Review

Authors: Carlo Catassi, Giulia Catassi, Loris Naspi...

PURPOSE OF REVIEW

To describe recent advances on nonceliac gluten sensitivity (NCGS), a recently described disorder characterized by variable symptoms and frequent irritable bowel syndrome (IBS)-like manifestations.

RECENT FINDINGS

The recent description of disease-triggering wheat components other than gluten, such as fructans and amylase-trypsin inhibitors (ATIs), definitely suggests that nonceliac wheat sensitivity (NCWS) is a better 'umbrella' terminology than NCGS. Self-reported NCWS is very common worldwide, particularly in patients seen at the gastroenterology clinic, but many of these diagnoses are not confirmed by standard clinical criteria. A biomarker of NCWS is still lacking, however, subtle histological features at the small intestinal biopsy may facilitate diagnosis. Treatment of NCWS is based on the gluten-free diet (GFD). The GFD has proven to be an effective treatment of a significant proportion of NCWS-related IBS patients. Dietary therapies for IBS, including the GFD, should be offered by dietitians who first assess dietary triggers and then tailor the intervention according to patient choice. Pioneer studies are under way to test the therapeutic efficacy of supplemental gluten-digesting enzyme preparations in patients with NCWS.

SUMMARY

Recent studies highlight interesting pathophysiological and clinical features of NCWS. Many questions remain, however, unanswered, such as the epidemiology, a biomarker(s), and the natural history of this clinical entity.

Topics: Humans; Irritable Bowel Syndrome; Malabsorption Syndromes; Glutens; Diet, Gluten-Free; Biomarkers; Celiac Disease

PubMed: 36942921
DOI: 10.1097/MCO.0000000000000925

Review

Authors: Charalampos Pierrakos, Jean-Louis Vincent

INTRODUCTION

Biomarkers can be useful for identifying or ruling out sepsis, identifying patients who may benefit from specific therapies or assessing the response to therapy.

METHODS

We used an electronic search of the PubMed database using the key words "sepsis" and "biomarker" to identify clinical and experimental studies which evaluated a biomarker in sepsis.

RESULTS

The search retrieved 3370 references covering 178 different biomarkers.

CONCLUSIONS

Many biomarkers have been evaluated for use in sepsis. Most of the biomarkers had been tested clinically, primarily as prognostic markers in sepsis; relatively few have been used for diagnosis. None has sufficient specificity or sensitivity to be routinely employed in clinical practice. PCT and CRP have been most widely used, but even these have limited ability to distinguish sepsis from other inflammatory conditions or to predict outcome.

Topics: Biomarkers; Humans; Sepsis

PubMed: 20144219
DOI: 10.1186/cc8872

Review

Authors: E V Schrezenmeier, J Barasch, K Budde...

Various biomarkers of acute kidney injury (AKI) have been discovered and characterized in the recent past. These molecules can be detected in urine or blood and signify structural damage to the kidney. Clinically, they are proposed as adjunct diagnostics to serum creatinine and urinary output to improve the early detection, differential diagnosis and prognostic assessment of AKI. The most obvious requirements for a biomarker include its reflection of the underlying pathophysiology of the disease. Hence, a biomarker of AKI should derive from the injured kidney and reflect a molecular process intimately connected with tissue injury. Here, we provide an overview of the basic pathophysiology, the cellular sources and the clinical performance of the most important currently proposed biomarkers of AKI: neutrophil gelatinase-associated lipocalin (NGAL), kidney injury molecule-1 (KIM-1), liver-type fatty acid-binding protein (L-FABP), interleukin-18 (IL-18), insulin-like growth factor-binding protein 7 (IGFBP7), tissue inhibitor of metalloproteinase 2 (TIMP-2) and calprotectin (S100A8/9). We also acknowledge each biomarker's advantages and disadvantages as well as important knowledge gaps and perspectives for future studies.

Topics: Acute Kidney Injury; Biomarkers; Humans

PubMed: 27474473
DOI: 10.1111/apha.12764

Authors: Yaping Shao, Tianbai Li, Zheyi Liu...

BACKGROUND

Parkinson's disease (PD) is a prevalent neurological disease in the elderly with increasing morbidity and mortality. Despite enormous efforts, rapid and accurate diagnosis of PD is still compromised. Metabolomics defines the final readout of genome-environment interactions through the analysis of the entire metabolic profile in biological matrices. Recently, unbiased metabolic profiling of human sample has been initiated to identify novel PD metabolic biomarkers and dysfunctional metabolic pathways, however, it remains a challenge to define reliable biomarker(s) for clinical use.

METHODS

We presented a comprehensive metabolic evaluation for identifying crucial metabolic disturbances in PD using liquid chromatography-high resolution mass spectrometry-based metabolomics approach. Plasma samples from 3 independent cohorts (n = 460, 223 PD, 169 healthy controls (HCs) and 68 PD-unrelated neurological disease controls) were collected for the characterization of metabolic changes resulted from PD, antiparkinsonian treatment and potential interferences of other diseases. Unbiased multivariate and univariate analyses were performed to determine the most promising metabolic signatures from all metabolomic datasets. Multiple linear regressions were applied to investigate the associations of metabolites with age, duration time and stage of PD. The combinational biomarker model established by binary logistic regression analysis was validated by 3 cohorts.

RESULTS

A list of metabolites including amino acids, acylcarnitines, organic acids, steroids, amides, and lipids from human plasma of 3 cohorts were identified. Compared with HC, we observed significant reductions of fatty acids (FFAs) and caffeine metabolites, elevations of bile acids and microbiota-derived deleterious metabolites, and alterations in steroid hormones in drug-naïve PD. Additionally, we found that L-dopa treatment could affect plasma metabolome involved in phenylalanine and tyrosine metabolism and alleviate the elevations of bile acids in PD. Finally, a metabolite panel of 4 biomarker candidates, including FFA 10:0, FFA 12:0, indolelactic acid and phenylacetyl-glutamine was identified based on comprehensive discovery and validation workflow. This panel showed favorable discriminating power for PD.

CONCLUSIONS

This study may help improve our understanding of PD etiopathogenesis and facilitate target screening for therapeutic intervention. The metabolite panel identified in this study may provide novel approach for the clinical diagnosis of PD in the future.

Topics: Aged; Biomarkers; Case-Control Studies; Chromatography, Liquid; Female; Humans; Male; Mass Spectrometry; Metabolome; Metabolomics; Parkinson Disease

PubMed: 33485385
DOI: 10.1186/s13024-021-00425-8

Review

Authors: Katarzyna Bartold, Zofia Iskierko, Piyush Sindhu Sharma...

Idiopathic pulmonary fibrosis (IPF) diagnosis is still the diagnosis of exclusion. Differentiating from other forms of interstitial lung diseases (ILDs) is essential, given the various therapeutic approaches. The IPF course is now unpredictable for individual patients, although some genetic factors and several biomarkers have already been associated with various IPF prognoses. Since its early stages, IPF may be asymptomatic, leading to a delayed diagnosis. The present review critically examines the recent literature on molecular biomarkers potentially useful in IPF diagnostics. The examined biomarkers are grouped into breath and sputum biomarkers, serologically assessed extracellular matrix neoepitope markers, and oxidative stress biomarkers in lung tissue. Fibroblasts and complete blood count have also gained recent interest in that respect. Although several biomarker candidates have been profiled, there has yet to be a single biomarker that proved specific to the IPF disease. Nevertheless, various IPF biomarkers have been used in preclinical and clinical trials to verify their predictive and monitoring potential.

Topics: Humans; Idiopathic Pulmonary Fibrosis; Biomarkers; Oxidative Stress

PubMed: 38657859
DOI: 10.1016/j.bj.2024.100729

The comparative and added prognostic value of biomarkers to the Revised Cardiac Risk Index for preoperative prediction of major adverse cardiac events and all-cause mortality in patients who undergo noncardiac surgery.

Authors: Lisette M Vernooij, Wilton A van Klei, Karel Gm Moons...

BACKGROUND

The Revised Cardiac Risk Index (RCRI) is a widely acknowledged prognostic model to estimate preoperatively the probability of developing in-hospital major adverse cardiac events (MACE) in patients undergoing noncardiac surgery. However, the RCRI does not always make accurate predictions, so various studies have investigated whether biomarkers added to or compared with the RCRI could improve this.

OBJECTIVES

Primary: To investigate the added predictive value of biomarkers to the RCRI to preoperatively predict in-hospital MACE and other adverse outcomes in patients undergoing noncardiac surgery. Secondary: To investigate the prognostic value of biomarkers compared to the RCRI to preoperatively predict in-hospital MACE and other adverse outcomes in patients undergoing noncardiac surgery. Tertiary: To investigate the prognostic value of other prediction models compared to the RCRI to preoperatively predict in-hospital MACE and other adverse outcomes in patients undergoing noncardiac surgery.

SEARCH METHODS

We searched MEDLINE and Embase from 1 January 1999 (the year that the RCRI was published) until 25 June 2020. We also searched ISI Web of Science and SCOPUS for articles referring to the original RCRI development study in that period.

SELECTION CRITERIA

We included studies among adults who underwent noncardiac surgery, reporting on (external) validation of the RCRI and: - the addition of biomarker(s) to the RCRI; or - the comparison of the predictive accuracy of biomarker(s) to the RCRI; or - the comparison of the predictive accuracy of the RCRI to other models. Besides MACE, all other adverse outcomes were considered for inclusion.

DATA COLLECTION AND ANALYSIS

We developed a data extraction form based on the CHARMS checklist. Independent pairs of authors screened references, extracted data and assessed risk of bias and concerns regarding applicability according to PROBAST. For biomarkers and prediction models that were added or compared to the RCRI in ≥ 3 different articles, we described study characteristics and findings in further detail. We did not apply GRADE as no guidance is available for prognostic model reviews.

MAIN RESULTS

We screened 3960 records and included 107 articles.   Over all objectives we rated risk of bias as high in ≥ 1 domain in 90% of included studies, particularly in the analysis domain. Statistical pooling or meta-analysis of reported results was impossible due to heterogeneity in various aspects: outcomes used, scale by which the biomarker was added/compared to the RCRI, prediction horizons and studied populations.  Added predictive value of biomarkers to the RCRI Fifty-one studies reported on the added value of biomarkers to the RCRI. Sixty-nine different predictors were identified derived from blood (29%), imaging (33%) or other sources (38%). Addition of NT-proBNP, troponin or their combination improved the RCRI for predicting MACE (median delta c-statistics: 0.08, 0.14 and 0.12 for NT-proBNP, troponin and their combination, respectively). The median total net reclassification index (NRI) was 0.16 and 0.74 after addition of troponin and NT-proBNP to the RCRI, respectively. Calibration was not reported. To predict myocardial infarction, the median delta c-statistic when NT-proBNP was added to the RCRI was 0.09, and 0.06 for prediction of all-cause mortality and MACE combined. For BNP and copeptin, data were not sufficient to provide results on their added predictive performance, for any of the outcomes. Comparison of the predictive value of biomarkers to the RCRI  Fifty-one studies assessed the predictive performance of biomarkers alone compared to the RCRI. We identified 60 unique predictors derived from blood (38%), imaging (30%) or other sources, such as the American Society of Anesthesiologists (ASA) classification (32%). Predictions were similar between the ASA classification and the RCRI for all studied outcomes. In studies different from those identified in objective 1, the median delta c-statistic was 0.15 and 0.12 in favour of  BNP and NT-proBNP alone, respectively, when compared to the RCRI, for the prediction of MACE. For C-reactive protein, the predictive performance was similar to the RCRI. For other biomarkers and outcomes, data were insufficient to provide summary results. One study reported on calibration and none on reclassification. Comparison of the predictive value of other prognostic models to the RCRI   Fifty-two articles compared the predictive ability of the RCRI to other prognostic models. Of these, 42% developed a new prediction model, 22% updated the RCRI, or another prediction model, and 37% validated an existing prediction model. None of the other prediction models showed better performance in predicting MACE than the RCRI. To predict myocardial infarction and cardiac arrest, ACS-NSQIP-MICA had a higher median delta c-statistic of 0.11 compared to the RCRI. To predict all-cause mortality, the median delta c-statistic was 0.15 higher in favour of ACS-NSQIP-SRS compared to the RCRI. Predictive performance was not better for CHADS, CHADS-VASc, RCHADS, Goldman index, Detsky index or VSG-CRI compared to the RCRI for any of the outcomes. Calibration and reclassification were reported in only one and three studies, respectively.

AUTHORS' CONCLUSIONS

Studies included in this review suggest that the predictive performance of the RCRI in predicting MACE is improved when NT-proBNP, troponin or their combination are added. Other studies indicate that BNP and NT-proBNP, when used in isolation, may even have a higher discriminative performance than the RCRI. There was insufficient evidence of a difference between the predictive accuracy of the RCRI and other prediction models in predicting MACE. However, ACS-NSQIP-MICA and ACS-NSQIP-SRS outperformed the RCRI in predicting myocardial infarction and cardiac arrest combined, and all-cause mortality, respectively. Nevertheless, the results cannot be interpreted as conclusive due to high risks of bias in a majority of papers, and pooling was impossible due to heterogeneity in outcomes, prediction horizons, biomarkers and studied populations. Future research on the added prognostic value of biomarkers to existing prediction models should focus on biomarkers with good predictive accuracy in other settings (e.g. diagnosis of myocardial infarction) and identification of biomarkers from omics data. They should be compared to novel biomarkers with so far insufficient evidence compared to established ones, including NT-proBNP or troponins. Adherence to recent guidance for prediction model studies (e.g. TRIPOD; PROBAST) and use of standardised outcome definitions in primary studies is highly recommended to facilitate systematic review and meta-analyses in the future.

Topics: Adult; Bias; Biomarkers; Heart Arrest; Humans; Myocardial Infarction; Peptide Fragments; Predictive Value of Tests; Prognosis; Risk Assessment

PubMed: 34931303
DOI: 10.1002/14651858.CD013139.pub2