Blood Protein Biomarkers Research Articles

Transdermal Minimally Invasive Optical Multiplex Detection of Protein Biomarkers by Nanopillars Array-Embedded Microneedles.

Biomarkers detection has become essential in medical diagnostics and early detection of life-threatening diseases. Modern-day medicine relies heavily on painful and invasive tests, with the extraction of large volumes of venous blood being the most common tool of biomarker detection. These tests are time-consuming, complex, expensive and require multiple sample manipulations and trained staff. The application of "intradermal" biosensors utilizing microneedles as minimally invasive sensing elements for capillary blood biomarkers detection has gained extensive interest in the past few years as a central point-of-care (POC) detection platform. Herein, we present a diagnosis paradigm based on vertically aligned nanopillar array-embedded microneedles sampling-and-detection elements for the direct optical detection and quantification of biomarkers in capillary blood. We present here a demonstration of the simple fabrication route for the creation of a multidetection-zone silicon nanopillar array, embedded in microneedle elements, followed by their area-selective chemical modification, toward the multiplex intradermal biomarkers detection. The utilization of the rapid and specific antibody-antigen binding, combined with the intrinsically large sensing area created by the nanopillar array, enables the simultaneous efficient ultrafast and highly sensitive intradermal capillary blood sampling and detection of protein biomarkers of clinical relevance, without requiring the extraction of blood samples for the ex vivo biomarkers analysis. Through preliminary in vitro and in vivo experiments, the direct intradermal in-skin blood extraction-free platform has demonstrated excellent sensitivity (low pM) and specificity for the accurate multiplex detection of protein biomarkers in capillary blood.

Host blood protein biomarkers to screen for tuberculosis disease: a systematic review and meta-analysis.

To our knowledge, this is the first comprehensive systematic review of host blood protein biomarkers for tuberculosis (TB), and we identified promising biomarkers for a TB screening test.

Where do we stand with screening for colorectal cancer and advanced adenoma based on serum protein biomarkers? A systematic review.

Colorectal cancer (CRC) screening has been proven to reduce both mortality and the incidence of this disease. Most CRC screening programs are based on fecal immunochemical tests (FITs), which have a low participation rate. Searching for blood protein biomarkers can lead to the development of a more accepted screening test. The aim of this systematic review was to compare the diagnostic potential of the most promising serum protein biomarkers. A systematic review based on PRISMA guidelines was conducted in the PubMed and Web of Science databases between January 2010 and December 2023. Studies assessing blood protein biomarkers for CRC screening were included. The sensitivity, specificity, and area under the ROC curve of each biomarker were collected. Among 4685 screened studies, 94 were considered for analysis. Most of them were case-control studies, leading to an overestimation of the performance of candidate biomarkers. The performance of no protein biomarker or combination of biomarkers appears to match that of the FIT. Studies with a suitable design and population, testing new assay techniques, or based on algorithms combining FIT with serum tests are needed.

Comprehensive serum proteomics profiles and potential protein biomarkers for the early detection of advanced adenoma and colorectal cancer.

The majority of colorectal cancer (CRC) cases develop from precursor advanced adenoma (AA). With the development of proteomics technologies, blood protein biomarkers have potential applications in the early screening of AA and CRC in the general population. To identify serum protein biomarkers for the early screening of AA and CRC. We collected 43 serum samples from 8 normal controls (NCs), 19 AA patients and 16 CRC patients at China-Japan Friendship Hospital. Quantitative proteomic analysis was performed using liquid chromatography-mass spectrometry/mass spectrometry and data independent acquisition, and differentially expressed proteins (DEPs) with P-values < 0.05 and absolute fold changes > 1.5 were screened out, followed by bioinformatics analysis. Prognosis was further analyzed based on public databases, and proteins expression in tissues were validated by immunohistochemistry. A total of 2132 proteins and 17365 peptides were identified in the serum samples. There were 459 upregulated proteins and 118 downregulated proteins in the NC vs AA group, 289 and 180 in the NC vs CRC group, and 52 and 248 in the AA vs CRC group, respectively. Bioinformatic analysis revealed that these DEPs had different functions and participated in extensive signaling pathways. We also identified DIAPH1, VASP, RAB11B, LBP, SAR1A, TUBGCP5, and DOK3 as important proteins for the progression of AA and CRC. Furthermore, VASP (P < 0.01), LBP (P = 0.01), TUBGCP5 (P < 0.01), and DOK3 (P < 0.01) were associated with a poor prognosis. In addition, we propose that LBP and VASP may be more promising protein biomarkers for the early screening of colorectal tumors. Our study elucidated the serum proteomic profiles of AA and CRC patients, and the identified proteins, such as LBP and VASP, may contribute to the early detection of AA and CRC.

Causal inference of the effect of blood proteome on the risk of head and neck cancer: two-sample Mendelian randomization

Early diagnosis of head and neck cancer can improve therapeutic outcomes but remains a challenge. The blood proteome can comprise a key source of biomarkers that enable the early diagnosis and precision medicine in head and neck cancer, but blood protein biomarkers of head and neck cancer are not well delineated. Here we applied two-sample Mendelian randomization to a GWAS dataset of 1478 blood proteins and large dataset of head and neck cancer cases and controls to identify blood proteome traits associated with head and neck cancer. Multiple two-sample Mendelian randomization (MR) methods were used to assess causal effects of the exposures, including: Inverse-variance weighted (IVW), Mendelian randomization-Egger method, Weight Median method, simple mode, weight mode. Sensitivity analysis was performed by using heterogeneity test, pleiotropy test and one-by-one exclusion test. Multivariable MR analyses were performed to assess the effects of obesity, diabetes mellitus, and smoking. A significant causal association between A Disintegrin and metalloproteinase domain-containing protein 23 (ADAM23) and head and neck cancer was noted. The sensitivity analysis indicated no significant bias. Multivariate analysis showed that the effect for ADAM23 remained significant after adjusting for the indirect effects of obesity, diabetes mellitus and smoking. In sum, this study showed a significant causal role of genetically dysregulated ADAM23 protein with head and neck cancer risk. The specific mechanisms underlying the role of ADAM23 in mediating head and neck cancer risk, and its role as a potential therapeutic target and biomarker, need further investigation.

Clinical risk factors and blood protein biomarkers of 10-year pneumonia risk.

Chronic inflammation may increase susceptibility to pneumonia. To explore associations between clinical comorbidities, serum protein immunoassays, and long-term pneumonia risk. Framingham Heart Study Offspring Cohort participants ≥65 years were linked to their Centers for Medicare Services claims data. Clinical data and 88 serum protein immunoassays were evaluated for associations with 10-year incident pneumonia risk using Fine-Gray models for competing risks of death and least absolute shrinkage and selection operators for covariate selection. We identified 1,370 participants with immunoassays and linkage to Medicare data. During 10 years of follow up, 428 (31%) participants had a pneumonia diagnosis. Chronic pulmonary disease [subdistribution hazard ratio (SHR) 1.87; 95% confidence interval (CI), 1.33-2.61], current smoking (SHR 1.79, CI 1.31-2.45), heart failure (SHR 1.74, CI 1.10-2.74), atrial fibrillation/flutter (SHR 1.43, CI 1.06-1.93), diabetes (SHR 1.36, CI 1.05-1.75), hospitalization within one year (SHR 1.34, CI 1.09-1.65), and age (SHR 1.06 per year, CI 1.04-1.08) were associated with pneumonia. Three baseline serum protein measurements were associated with pneumonia risk independent of measured clinical factors: growth differentiation factor 15 (SHR 1.32; CI 1.02-1.69), C-reactive protein (SHR 1.16, CI 1.06-1.27) and matrix metallopeptidase 8 (SHR 1.14, CI 1.01-1.30). Addition of C-reactive protein to the clinical model improved prediction (Akaike information criterion 4950 from 4960; C-statistic of 0.64 from 0.62). Clinical comorbidities and serum immunoassays were predictive of pneumonia risk. C-reactive protein, a routinely-available measure of inflammation, modestly improved pneumonia risk prediction over clinical factors. Our findings support the hypothesis that prior inflammation may increase the risk of pneumonia.

Temporal Assessment of Protein Stability in Dried Blood Spots.

The use of protein biomarkers in blood for clinical settings is limited by the cost and accessibility of traditional venipuncture sampling. The dried blood spot (DBS) technique offers a less invasive and more accessible alternative. However, protein stability in DBS has not been well evaluated. Herein, we deployed a quantitative LC-MS/MS system to construct proteomic atlases of whole blood, DBSs, plasma, and blood cells. Approximately 4% of detected proteins' abundance was significantly altered during blood drying into blood spots, with overwhelming disturbances in cytoplasmic fraction. We also reported a novel finding suggesting a decrease in the level of membrane/cytoskeletal proteins (SLC4A1, RHAG, DSC1, DSP, and JUP) and an increase in the level of proteins (ATG3, SEC14L4, and NRBP1) related to intracellular trafficking. Furthermore, we identified 19 temporally dynamic proteins in DBS samples stored at room temperature for up to 6 months. There were three declined cytoskeleton-related proteins (RDX, SH3BGRL3, and MYH9) and four elevated proteins (XPO7, RAN, SLC2A1, and SLC29A1) involved in cytoplasmic transport as representatives. The instability was governed predominantly by hydrophilic proteins and enhanced significantly with an increasing storage time. Our analyses provide comprehensive knowledge of both short- and long-term storage stability of DBS proteins, forming the foundation for the widespread use of DBS in clinical proteomics and other analytical applications.

Tau, Glial Fibrillary Acidic Protein, and Neurofilament Light Chain as Brain Protein Biomarkers in Cerebrospinal Fluid and Blood for Diagnosis of Neurobiological Diseases.

Neurological damage is the pathological substrate of permanent disability in various neurodegenerative disorders. Early detection of this damage, including its identification and quantification, is critical to preventing the disease's progression in the brain. Tau, glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL), as brain protein biomarkers, have the potential to improve diagnostic accuracy, disease monitoring, prognostic assessment, and treatment efficacy. These biomarkers are released into the cerebrospinal fluid (CSF) and blood proportionally to the degree of neuron and astrocyte damage in different neurological disorders, including stroke, traumatic brain injury, multiple sclerosis, neurodegenerative dementia, and Parkinson's disease. Here, we review how Tau, GFAP, and NfL biomarkers are detected in CSF and blood as crucial diagnostic tools, as well as the levels of these biomarkers used for differentiating a range of neurological diseases and monitoring disease progression. We also discuss a biosensor approach that allows for the real-time detection of multiple biomarkers in various neurodegenerative diseases. This combined detection system of brain protein biomarkers holds significant promise for developing more specific and accurate clinical tools that can identify the type and stage of human neurological diseases with greater precision.

Abstract PO5-07-08: A Serum Biomarker for the Early Detection of Breast cancer

Abstract Background: After decades of use, clinicians and medical societies are still debating on the clinical utility of mammography, the standard of care for breast cancer (BC) screening, because of its limitations such as subjective visual interpretation, high rate of false-positive and false-negative (in dense breast) and concerns over radiation exposure. There are 55 million women, and about 45% of them considered dense breast, in need of an accurate, easy to use test for the screening of BC regardless of breast tissue complexities to differentiate between BC and benign findings such as calcification and fibrosis. To this date there is no blood protein biomarker (BM) in clinical use for the early detection of BC. We have developed a novel BC specific protein biomarker -based blood test to detect patients with early-stage BC and established herein the diagnostic performance of our BM in discriminating BC patients from healthy individuals and benign breast cases. Methods: A total of 433 serum samples (318 primary BC, 32 benign breast and 83 normal breast) were obtained from the Cooperative Human Tissue Network and from the IRCCS San Raffaele Pisana Research Center (blind samples). 247 BC samples were provided as blind samples and were used to validate the screening utility. Serum samples were tested with our BC specific BM by sandwich ELISA. Receiver operating characteristic (ROC) curves were calculated to evaluate the diagnostic performance, by determining sensitivity (SE), specificity (SP), area under the curve (AUC), and confidence interval (CI). Statistical analysis, ROC curves and scatter plots were performed with GraphPad Prism 7.0. Results: BM elevation in early cancer cases (stage I-II, Ductal Carcinoma) was found to be statistically significant (p&amp;lt; 0.0001) as compared to healthy and benign cases. Our test discriminated early BC patients from non-cancer cases with 87.3% SE at 85.2% SP and was further validated in a blind set including 247 BC (Ductal &amp; Lobular Carcinoma, stage I-III), 30 benign and 83 normal breast cases (88%SE, 86%SP). The sensitivity for early-stage (I-II) and late stage (III) BC was 86.8% and 94.3%, respectively. While lobular carcinoma is particularly not well detected by mammography with a sensitivity as low as 57% and much lower for women with dense breast, it was detected with high sensitivity (90%) in our test. 80% of fibrocystic changes (FCC), the most common form of benign breast disease in women over 50 years of age were negative with our test. Although it is a benign condition, FCC may be misdiagnosed as a malignant lesion by physical examination and imaging. Conclusion: Our results support the utility of our blood-based test for the early detection of breast cancer which presents improved features over mammography such as higher lobular cancer sensitivity, lower detection rate of common form of benign condition often misdiagnosed by imaging, absence of radiation and expected high compliance. Further testing is needed to confirm accuracy of our test in the dense breast population. Citation Format: Christine Chavany, Jeffrey Dea, Monica Guevara, Rafael Hernandez-Gonzalez, Patrizia Ferroni, Fiorella Guadagni, Rosaura Valle, Moncef Jendoubi. A Serum Biomarker for the Early Detection of Breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-07-08.

A systematic review and meta-analysis of major blood protein biomarkers that predict unfavorable outcomes in severe traumatic brain injury

IntroductionSevere traumatic brain injury (TBI) presentation and late clinical outcomes are usually evaluated by the Glasgow Outcome Scale-Extended (GOS-E), which lacks strong prognostic predictability. Several blood biomarkers have been linked to TBI, such as Tau, GFAP, UCH-L1, S-100B, and NSE. Clinical values of TBI biomarkers have yet to be evaluated in a focused multi-study meta-analysis. We reviewed relevant articles evaluating potential relationships between TBI biomarkers and both early and 6-month outcomes. MethodsAll PubMed article publications from January 2000 to November 2023 with the search criteria “Protein Biomarker” AND “Traumatic Brain Injury” were included. Amongst all comparative studies, the sensitivity means and range values of biomarkers in predicting CT Rotterdam scores, ICU admission in the early period, or predicting GOS-E < 4 at the 6-month period were calculated from confusion matrices. Sensitivity values were modeled for each biomarker across studies and compared statistically for heterogeneity and differences. ResultsFrom the 65 articles that met the criteria, 13 were included in this study. Six articles involved early-period TBI outcomes and seven involved 6-month outcomes. In the early period TBI outcomes, GFAP had a superior sensitivity to UCH-L1 and S-100B, and similar sensitivity to the CT Rotterdam score. In the 6-month period TBI outcomes, total Tau and NSE both had significant interstudy heterogeneity, making them inferior to GFAP, phosphorylated Tau, UCH-L1, and S-100B, all four of which had similar sensitivities at 75 %. This sensitivity range at 6-month outcomes was still relatively inferior to the CT Rotterdam score. Total Tau did not show any prognostic advantage at six months with GOS-E < 4, and phosphorylated Tau was similar in its sensitivity to other biomarkers such as GFAP and UCH-L1 and still inferior to the CT Rotterdam score. ConclusionThis data suggests that TBI protein biomarkers do not possess better prognostic value with regards to outcomes.

The role of blood protein biomarkers in acute ischemic stroke prognosis

BackgroundStroke is one of the most frequent causes of mortality and disability, blood protein biomarkers are used to determine patients at high risk for a severe illness and to estimate the outcome. This study aimed to detect the relation between serum levels of C-reactive protein, matrix metalloproteinase 9, S100 calcium-binding protein B, brain natriuretic peptide, D-dimer and stroke severity and outcome in acute ischemic stroke patients.ResultsOne hundred eighty-six patients with acute ischemic stroke participated and were subjected to complete general, neurological examination, assessment of stroke severity clinically and radiologically using National Institute of Health Stroke Scale (NIHSS), and Alberta Stroke Program Early CT (ASPECT) score and assessment of functional outcome using (modified Rankin Scale). C-reactive protein, matrix metalloproteinase 9, S100 calcium-binding protein B, brain natriuretic peptide and D-dimer were assessed. Higher C-reactive protein was found in patients with ASPECT score ≤ 7 and in patients with cerebral edema, seizures and was positively correlated with stroke severity according to NIHSS and modified Rankin Scale. C-reactive protein serum level at onset was negatively correlated with NIHSS at onset and is a significant predictor for mortality. D-dimer was negatively correlated with NIHSS. S100 calcium-binding protein B was significantly elevated in patients who developed hemorrhagic transformation.ConclusionsSerum C-reactive protein level can be used as a predictor for mortality and higher S100 calcium-binding protein B was detected in patients with hemorrhagic transformation.

Abstract 6153: Proteome-wide association study and functional validation identify novel protein markers for pancreatic ductal adenocarcinoma

Abstract Pancreatic ductal adenocarcinoma (PDAC) remains a lethal malignancy, largely due to the paucity of reliable biomarkers for early detection and therapeutic targeting. Existing blood protein biomarkers for PDAC often suffer from replicability issues, arising from inherent limitations such as unmeasured confounding factors in conventional epidemiologic study designs. To circumvent these limitations, we use genetic instruments to identify proteins with genetically predicted levels to be associated with PDAC risk. Leveraging genome and plasma proteome data from the INTERVAL study, we established and validated models to predict protein levels using genetic variants. By examining 8,275 PDAC cases and 6,723 controls, we identified 40 associated proteins, of which 16 are novel. Functionally validating these candidates by focusing on two selected novel protein-encoding genes, GOLM1 and B4GALT1, we demonstrated their pivotal roles in driving PDAC cell proliferation, migration, and invasion. Furthermore, we also identified potential drug repurposing opportunities for treating PDAC. Our dual approach enhances our understanding of PDAC etiology and potentially opens new avenues for therapeutic interventions. Citation Format: Jingjing Zhu, Ke Wu, Shuai Liu, Alexandra Masca, Hua Zhong, Tai Yang, Dalia H. Ghoneim, Praveen Surendran, Tanxin Liu, Qizhi Yao, Tao Liu, Sarah Fahle, Adam Butterworth, Md A. Alam, Jaydutt V. Vadgama, Youping Deng, Hong-Wen Deng, Chong Wu, Yong Wu, Lang Wu. Proteome-wide association study and functional validation identify novel protein markers for pancreatic ductal adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6153.

Using blood transcriptome analysis for Alzheimer's disease diagnosis and patient stratification.

Blood protein biomarkers demonstrate potential for Alzheimer's disease (AD) diagnosis. Limited studies examine the molecular changes in AD blood cells. Bulk RNA-sequencing of blood cells was performed on AD patients of Chinese descent (n=214 and 26 in the discovery and validation cohorts, respectively) with normal controls (n=208 and 38 in the discovery and validation cohorts, respectively). Weighted gene co-expression network analysis (WGCNA) and deconvolution analysis identified AD-associated gene modules and blood cell types. Regression and unsupervised clustering analysis identified AD-associated genes, gene modules, cell types, and established AD classification models. WGCNA on differentially expressed genes revealed 15 gene modules, with 6 accurately classifying AD (areas under the receiver operating characteristics curve [auROCs]>0.90). These modules stratified AD patients into subgroups with distinct disease states. Cell-type deconvolution analysis identified specific blood cell types potentially associated with AD pathogenesis. This study highlights the potential of blood transcriptome for AD diagnosis, patient stratification, and mechanistic studies. We comprehensively analyze the blood transcriptomes of a well-characterized Alzheimer's disease cohort to identify genes, gene modules, pathways, and specific blood cells associated with the disease. Blood transcriptome analysis accurately classifies and stratifies patients with Alzheimer's disease, with some gene modules achieving classification accuracy comparable to that of the plasma ATN biomarkers. Immune-associated pathways and immune cells, such as neutrophils, have potential roles in the pathogenesis and progression of Alzheimer's disease.

Identification of blood protein biomarkers associated with prostate cancer risk using genetic prediction models: analysis of over 140,000 subjects.

Prostate cancer (PCa) brings huge public health burden in men. A growing number of conventional observational studies report associations of multiple circulating proteins with PCa risk. However, the existing findings may be subject to incoherent biases of conventional epidemiologic studies. To better characterize their associations, herein, we evaluated associations of genetically predicted concentrations of plasma proteins with PCa risk. We developed comprehensive genetic prediction models for protein levels in plasma. After testing 1308 proteins in 79 194 cases and 61 112 controls of European ancestry included in the consortia of BPC3, CAPS, CRUK, PEGASUS, and PRACTICAL, 24 proteins showed significant associations with PCa risk, including 16 previously reported proteins and eight novel proteins. Of them, 14 proteins showed negative associations and 10 showed positive associations with PCa risk. For 18 of the identified proteins, potential functional somatic changes of encoding genes were detected in PCa patients in The Cancer Genome Atlas (TCGA). Genes encoding these proteins were significantly involved in cancer-related pathways. We further identified drugs targeting the identified proteins, which may serve as candidates for drug repurposing for treating PCa. In conclusion, this study identifies novel protein biomarker candidates for PCa risk, which may provide new perspectives on the etiology of PCa and improve its therapeutic strategies.

Filgotinib Modulates Inflammation-Associated Peripheral Blood Protein Biomarkers in Adults with Active Rheumatoid Arthritis and Prior Inadequate Response to Methotrexate.

Our aim was to evaluate protein biomarker changes related to the administration of filgotinib, a Janus kinase (JAK) 1 preferential inhibitor, in patients with moderately to severely active rheumatoid arthritis (RA) with inadequate response to methotrexate. Plasma and serum samples were collected from patients enrolled in FINCH 1 (NCT02889796), a Phase 3 trial. Patients with stable backgrounds of methotrexate were randomly assigned once-daily oral filgotinib 200 or 100mg, subcutaneous adalimumab 40mg every 2weeks (W), or placebo. Up to 35 biomarkers were analyzed at baseline, W4, and W12 with enzyme-linked immunosorbent assays and chemiluminescence and electrochemiluminescence assays. At baseline, four distinct biomarker clusters were identified. The strongest intragroup correlations were in bone-cartilage resorption/inflammation and JAK/signal transducer and activator of transcription (STAT) signaling activity. At baseline, significant positive correlations were identified for cytokines with patient-reported pain and with patient measures of fatigue. Filgotinib reduced levels of cytokines associated with inflammation and cell migration as early as W4 and through W12. Compared to adalimumab, filgotinib induced significant reductions in bone-related turnover biomarkers, N-telopeptide of type 1 collagen and C-telopeptide 1, as well as biomarkers associated with baseline disease activity. No baseline predictors of therapeutic response to filgotinib were identified. Filgotinib reduced peripheral protein biomarkers associated with JAK/STAT signaling, inflammatory signaling, immune cell migration, and bone resorption as soon as W4 in FINCH 1. Effects were dose-dependent and consistent with the clinical efficacy of filgotinib observed in FINCH 1. The changes in peripheral biomarkers associated with filgotinib treatment in methotrexate-experienced patients are consistent with changes observed in both methotrexate-naïve and biologic disease-modifying antirheumatic drug-experienced RA populations. These data demonstrate dose-dependent effects of preferential JAK1 inhibition by filgotinib on peripheral blood protein biomarkers in methotrexate-experienced patients with RA. ClinicalTrials.gov, NCT02889796.

A wireless patch for the monitoring of C-reactive protein in sweat.

The quantification of protein biomarkers in blood at picomolar-level sensitivity requires labour-intensive incubation and washing steps. Sensing proteins in sweat, which would allow for point-of-care monitoring, is hindered by the typically large interpersonal and intrapersonal variations in its composition. Here we report the design and performance of a wearable and wireless patch for the real-time electrochemical detection of the inflammatory biomarker C-reactive (CRP) protein in sweat. The device integrates iontophoretic sweat extraction, microfluidic channels for sweat sampling and for reagent routing and replacement, and a graphene-based sensor array for quantifying CRP (via an electrode functionalized with anti-CRP capture antibodies-conjugated gold nanoparticles), ionic strength, pH and temperature for the real-time calibration of the CRP sensor. In patients with chronic obstructive pulmonary disease, with active or past infections or who had heart failure, the elevated concentrations of CRP measured via the patch correlated well with the protein's levels in serum. Wearable biosensors for the real-time sensitive analysis of inflammatory proteins in sweat may facilitate the management of chronic diseases.

Protein Biomarkers in Chronic Kidney Disease in Children-What Do We Know So Far?

Chronic kidney disease (CKD) in children is a major concern of medical care and public health as it is related to high morbidity and mortality due to progression to end-stage kidney disease (ESKD). It is essential to identify patients with a risk of developing CKD to implement therapeutic interventions. Unfortunately, conventional markers of CKD, such as serum creatinine, glomerular filtration rate (GFR) and proteinuria, have many limitations in serving as an early and specific diagnostic tool for this condition. Despite the above, they are still the most frequently utilized as we do not have better. Studies from the last decade identified multiple CKD blood and urine protein biomarkers but mostly assessed the adult population. This article outlines some recent achievements and new perspectives in finding a set of protein biomarkers that might improve our ability to prognose CKD progression in children, monitor the response to treatment, or even become a potential therapeutic target.

Plasma p‐tau181 for the differential diagnosis of cognitive impairment in a prospective, daily clinical practice cohort

AbstractBackgroundCurrently used biomarkers for the differential diagnosis of cognitive impairment are expensive and/or relatively invasive, limiting their availability to the general population. Blood protein biomarkers have showed promising results for screening, differential diagnosis, and prognosis. We aimed to study the diagnostic performance of plasma p‐tau181 in a daily clinical practice, prospective memory clinic cohort.MethodAll patients referred for a first clinical evaluation with suspected cognitive impairment between January 1st, 2020 and March 30th, 2021, were invited to participate in the study. Plasma p‐tau181 was measured using SIMOA technology (Quanterix). Clinical diagnoses were made following current diagnostic criteria and blinded to plasma p‐tau181 results.ResultA total of 232 participants were recruited (mean age 69y, mean MMSE 24), including 25 cognitively unimpaired (CU) controls (mean age 67y, MMSE 28). Clinical diagnoses were AD (82 subjects, 43 of them with prodromal AD [CDR = 0.5]), 83 non‐neurodegenerative cognitive impairment (non‐ND, 75 of them CDR = 0.5), 22 frontotemporal dementia (FTD) and 20 Lewy body disease (LBD).Plasma p‐tau181 levels were statistically higher in AD (mean 2.39 pg/mL) compared with CU (mean 1.09 pg/mL), non‐ND (mean 1.43 pg/mL), FTD (mean 1.69 pg/mL) and LBD (mean 1.64 pg/mL) with a relatively good diagnostic performance for the differential diagnosis between AD and CU, non‐ND, LBD and FTD (AUC of 0.89, 0.80, 0.73 and 0.71, respectively).128 subjects (55% of the cohort) had specific AD biomarkers (CSF or PET) available. In this subgroup, p‐tau181 differentiated AD from CU and non‐ND (AUC 0.91 and 0.92, respectively) and prodromal AD from CDR = 0.5 non‐ND participants (2.32 vs 1.02 pg/mL, p&lt;0.001, AUC 0.90). Plasma p‐tau181 discriminated between a positive and negative amyloid beta status (defined by CSF/PET) with an AUC of 0.87 and AD subjects from those with a clinical diagnosis of LBD and FTD who had a negative amyloid beta status (AUC of 0.94 and 0.86, respectively). Plasma p‐tau181 correlated with CSF p‐tau181 (rs = 0.48, p&lt;0.001).ConclusionIn our cohort of everyday clinical practice, plasma p‐tau181 is an accurate biomarker for predicting the AD pathophysiological process and discriminating AD from other neurodegenerative and non‐neurodegenerative causes of cognitive impairment.

Protein biomarkers of disease progression in patients with systemic sclerosis associated interstitial lung disease

Systemic sclerosis is a rare connective tissue disease; and interstitial lung disease (SSc–ILD) is associated with significant morbidity and mortality. There are no clinical, radiologic features, nor biomarkers that identify the specific time when patients are at risk for progression at which the benefits from treatment outweigh the risks. Our study aimed to identify blood protein biomarkers associated with progression of interstitial lung disease in patients with SSc–ILD using an unbiased, high-throughput approach. We classified SSc–ILD as progressive or stable based on change in forced vital capacity over 12 months or less. We profiled serum proteins by quantitative mass spectrometry and analyzed the association between protein levels and progression of SSc–ILD via logistic regression. The proteins associated with at a p value of < 0.1 were queried in the ingenuity pathway analysis (IPA) software to identify interaction networks, signaling, and metabolic pathways. Through principal component analysis, the relationship between the top 10 principal components and progression was evaluated. Unsupervised hierarchical clustering with heatmapping was done to define unique groups. The cohort consisted of 72 patients, 32 with progressive SSc–ILD and 40 with stable disease with similar baseline characteristics. Of a total of 794 proteins, 29 were associated with disease progression. After adjusting for multiple testing, these associations did not remain significant. IPA identified five upstream regulators that targeted proteins associated with progression, as well as a canonical pathway with a higher signal in the progression group. Principal component analysis showed that the ten components with the highest Eigenvalues represented 41% of the variability of the sample. Unsupervised clustering analysis revealed no significant heterogeneity between the subjects. We identified 29 proteins associated with progressive SSc–ILD. While these associations did not remain significant after accounting for multiple testing, some of these proteins are part of pathways relevant to autoimmunity and fibrogenesis. Limitations included a small sample size and a proportion of immunosuppressant use in the cohort, which could have altered the expression of inflammatory and immunologic proteins. Future directions include a targeted evaluation of these proteins in another SSc–ILD cohort or application of this study design to a treatment naïve population.

A Novel Combination of Host Protein Biomarkers to Distinguish Bacterial From Viral Infections in Febrile Children in Emergency Care.

Distinguishing bacterial and viral infections based on clinical symptoms in febrile children attending the emergency department (ED) is challenging. The aim of this study is to determine a novel combination of host protein biomarkers and to assess its performance in distinguishing between bacterial and viral infection in febrile children attending EDs. A literature search was performed to identify blood protein biomarkers able to distinguish bacterial and viral infections (May 2015-May 2019). We selected 7 protein biomarkers: Procalcitonin, TNF-related apoptosis-inducing ligand (TRAIL), interleukin (IL)-4, IL-6, Interferon gamma-induced protein-10 (CXCL-10), interferon-gamma and lipocalin 2 (LCN2). These were measured in blood plasma using a bead-based immunoassay in children with a confirmed bacterial or viral infection attending EDs in the Netherlands. We used generalized linear modeling to classify bacterial and viral infections and applied a previously developed feature selection algorithm to select the optimal combination of proteins. We performed a subgroup analysis of this protein signature in patients with C-reactive protein <60 mg/L, representing a clinically challenging diagnostic group. In total 102 children were included (N = 67 bacterial; N = 35 viral). Individual performance of the 7 biomarkers in classifying bacterial versus viral infections ranged from 60.8%-74.5% area under the receiver operator curve (AUC). TRAIL, LCN2 and IL-6 were identified as the best 3-protein signature with an AUC of 86% (95% CI: 71.3%-100%). In 57 patients with C-reactive protein levels <60 mg/L, the 3-protein signature had an AUC of 85.1% (95% CI: 75.3%-94.9%). We demonstrate a promising novel combination of 3 host protein biomarkers; TRAIL, LCN2 and IL-6, which performs well in classifying bacterial and viral infections in febrile children in emergency care.