SEARCH · Search PubMed

Results for “Enzymatic biomarkers”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

183 records · Page 2Linked to original sources

PubMed2026

MicroRNAs in Oral Bio-Fluids as Predictive Biomarkers of Orthodontic Tooth Movement: A Systematic Review.

This systematic review was designed to assess scientific evidence of the association of microRNA expression during orthodontic tooth movement through various time points. A systematic review was performed in accordance with the PRISMA checklist. A search strategy was developed in electronic databases including Med Line, Scopus, EBSCO Host and ProQuest Dissertations & Theses Global until June 2025. Eligibility criteria included studies that investigated microRNA expression in saliva/GCF during orthodontic treatment. The risk of bias of the included studies was analysed using the QUADAS-2 and RoB-2 tools. The search retrieved 2800 records, of which nine studies were selected. Minor variations in GCF collection were noted, while stimulated saliva was collected in one study. RT-PCR and the Fluro meter accounted for the majority of miRNA estimation. Thirteen miRNAs were identified as target biomarkers for OTM regulation. Despite the high risk of bias, the evidence from the current systematic review indicates that microRNAs can be considered as potential biomarkers of orthodontic tooth movement in oral biofluids. Trial Registration: Prospero ID-CRD420251153064.

Humans↗

PubMed2026

Revealing potential biomarkers and metabolic mechanisms of ovarian aging in hens during late laying period based on machine learning and metabolomics.

Ovarian function decline during the late laying period represents a major bottleneck for the economic efficiency of the global poultry industry. However, the underlying metabolic mechanisms and reliable early-warning biomarkers for ovarian aging remain poorly understood. In this study, we performed the first untargeted LC-MS/MS metabolomics analysis of ovarian tissues from Taihe silky fowls at peak laying (30 weeks) and late laying (50 weeks) stages, and employed an ensemble machine learning strategy integrating LASSO, random forest, and support vector machine (SVM) algorithms to identify high-confidence core biomarkers of ovarian aging. Gene expression analysis was further conducted to validate the potential molecular mechanisms. Our results showed that the metabolic profiles of ovarian tissues differed significantly between the two groups. A total of 6 core biomarkers were identified, 4 of which were long-chain acylcarnitines. Mechanistic analysis revealed that downregulation of key genes in the carnitine shuttle system led to impaired mitochondrial fatty acid β-oxidation, which in turn triggered excessive oxidative stress and compromised ovarian endocrine function. In conclusion, this study identifies long-chain acylcarnitines as potential metabolic biomarkers for ovarian aging in Taihe silky fowls. These findings provide novel insights into the metabolic basis of poultry ovarian aging and lay a theoretical foundation for the precise regulation of reproductive performance in indigenous poultry breeds.

Animals↗

PubMed2026

Integrated multi-omics profiling of amniotic fluid identifies predictive biomarkers for fetal growth restriction trajectories.

BACKGROUND: Fetal growth restriction (FGR) is a complex condition with highly heterogeneous clinical outcomes, making prenatal distinction between transient and persistent growth failure challenging. This study aims to identify amniotic fluid (AF) biomarkers capable of differentiating distinct FGR trajectories and characterizing persistent growth failure mechanisms. METHODS: Integrated proteomic and metabolomic profiling was performed on AF samples from transient FGR (n = 11), persistent FGR (n = 9), and healthy controls (n = 13). Diagnostic and prognostic models were developed using multivariate analysis. Selected protein candidates were validated via ELISA in an independent cohort (n = 69). RESULTS: Multi-omics analysis revealed distinct molecular signatures for FGR stratification. A two-protein diagnostic panel (PDGFA and phospho-STAT5A) achieved an AUC of 1.000 in the discovery stage and 0.780 in the external validation cohort. For prognostic assessment, a molecular signature including IREB2, HLA-C, and PLXNB2 accurately predicted persistent growth failure from transient recovery (AUC = 0.966). Cross-platform integration highlighted the mass spectrometry-derived WASHC2C as a central hub protein with a significant progressive increase across the control, transient, and persistent groups (p < 0.001). CONCLUSIONS: This study establishes a multi-omics framework for prenatal FGR stratification. Our findings identify distinct molecular signatures reflecting the intrauterine environment and provide high-performance molecular tools for predicting divergent fetal growth trajectories to guide personalized clinical decision-making.

Humans↗

PubMed2026

A genome-wide coverage-based pipeline for the identification of host-derived candidate DNA biomarkers from cell-free blood.

We have created a new data-analysis pipeline for the discovery of host-specific candidate DNA biomarkers derived from sequencing data of cell-free blood. Unlike approaches that rely on specific molecular or genetic signatures, our method leverages the coverage distribution of cell-free DNA sequences mapped to a reference genome, applying statistical analyses to identify informative short genomic regions for biomarker discovery. The pipeline is applicable to diverse diseases and can be used to analyze cell-free DNA sequences from plasma or serum to identify candidate biomarkers that are characteristic of disease states in mammals. Core functionalities were developed in Java and integrated with open-source software tools for the preprocessing of raw sequencing data, complemented by Python scripts for the machine-learning analysis and statistical validation. The pipeline is designed for HPC use and users can access the pipeline through a Galaxy workflow, which offers a user-friendly web interface for input selection prior to execution and analysis progress monitoring. Performance tests, carried out using duplicate sets of COVID-19 samples and controls, showed linear scalability of execution time with an increasing dataset size, as well as a substantial reduction in execution time through parallelized computation, whereby each HPC node is used to process the data of one chromosome. Further statistical tests confirmed the quality of the pipeline's results by showing that the set of identified candidate biomarkers remained stable across varying dataset sizes.

Biomarkers↗

PubMed2026

Machine learning-ready genomic biomarkers: ATF3 polymorphisms predict postoperative analgesic demand through AI-compatible phenotyping.

PURPOSE: To determine whether ATF3 polymorphisms can serve as genetic biomarkers for machine learning-based precision analgesia by establishing a genotype-phenotype association suitable for predictive modeling of postoperative opioid requirements. METHODS: In a prospective cohort of 167 adults undergoing abdominal surgery, ATF3 SNPs rs3122721 and rs3125293 were genotyped. A structured dataset architecture was developed to represent genetic profiles as input features for supervised learning models, enabling translational analysis of genotype‑dependent opioid consumption over 72 h. RESULTS: Patients with homozygous genotypes of the ATF3 SNPs had significantly higher opioid requirements than non‑carriers, despite reporting similar subjective pain scores. This consistent genotype‑dependent pattern provided a clinically relevant phenotype suitable for integration into predictive algorithms. CONCLUSION: ATF3 genotyping offers a promising biomarker for computationally informed precision analgesia. By linking genomic variability to clinically meaningful outcomes within a structured clinical and genomic framework, this approach supports the future development of risk-stratified clinical decision-support systems to optimize postoperative pain management.Trial registration ChiCTR1900021991, registered 30 April 2019. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at https://doi.org/10.1007/s13755-026-00480-9.

ATF3↗

PubMed2026

Herbicolin A, an antifungal lipopeptide produced by Pantoea agglomerans APC 4211 is a promising biocontrol agent against food spoilage fungi.

Fungal contamination of food with yeast and molds is associated with major economic losses due to spoilage and also poses health risks in the form of mycotoxin production. The strain Pantoea agglomerans APC 4211 isolated from leaves of Ilex aquifolium (holly tree) has broad spectrum antifungal activity against a variety of food spoilage fungi. Genomic analysis of the strain confirmed the presence of biosynthetic gene clusters potentially encoding for the enzymatic machinery required for the production of the antifungal lipopeptide herbicolin A. Matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) analysis of the cell-free supernatant (CFS) confirmed the presence of molecular masses corresponding to herbicolin A (1300.8 Da), and herbicolin B (1138 Da). Purified herbicolin A has desirable properties for biotechnological applications, including potent antifungal activity against a range of spoilage fungi, thermal stability and resistance to proteases. The lipopeptide has low cytotoxicity against epithelial cell lines and has minimum inhibitory concentrations (MICs) lower than those of some commercial antifungal drugs (0.2-2.5 mg/L). In a model dairy system (10% skim milk), herbicolin A demonstrated excellent solubility and stability, effectively eliminating Aspergillus niger and Penicillium notatum at a concentration of 5 mg/L. Overall, the study determines herbicolin's A spectrum against food spoilage organisms and examines potential applications in food. In conclusion, herbicolin A is a potent, naturally occurring antifungal agent with the potential to be applied as a biopreservative in food systems, providing a safe, clean-label, and efficient compound for synthetic preservatives replacement.

Pantoea↗

PubMed2026

Quality assessment, prognostic factors, and biomarkers for brain tumor analysis: a comprehensive systematic review.

The brain tumors possess different causative factors and properties, making their diagnosis and treatment difficult. Growth of these cancers usually leads to compression of the adjacent nerves and obstruction of the flow of cerebrospinal fluid, thus leading to increase in intracranial pressure. This affects the working of brain in many ways; thus, the difficulty involved in its treatment. With the improvements in technology in neuroimaging, including Diffusion Tensor Imaging (DTI), Positron Emission Tomography (PET), and multiparametric Magnetic Resonance Imaging (mpMRI), the diagnosis process has become easy. The effectiveness of any form of therapy in such patients depends primarily on their prognosis. While it is a common practice that physicians determine the prognosis of the disease by considering the age of the patient, histological grade of the tumor, and resection status, now this method has become more comprehensive by adding molecular signature and genetic analyses to the list of criteria. Next-generation sequencing (NGS) allows a reliable molecular classification. It increases the level of risk stratification, facilitating the application of therapies tailored to individual patients. Thus, molecular oncology has greatly changed our views on brain tumors' pathology and prognosis while neoadjuvant treatments aim at increasing the survival rate. On the other hand, radiogenomics is a field of study that combines non-invasive imaging phenotypes and genomic information in order to find unique molecular signatures of tumors without collecting samples from tumors. Molecular biomarkers are absolutely essential in the diagnosis of cancer, treatment monitoring, and recurrence of cancer. Advances in liquid biopsy technology, particularly the methods for circulating tumor DNA (ctDNA) and Extracellular Vesicle (EV) based analysis, have enabled the possibility of non-invasive monitoring of the progression of the tumors over time. This review highlights key studies and important scientific works about imaging technologies, biomarkers, and prognostic factors of malignant brain tumors.

Humans↗

PubMed2026

Plasma proteomics reveal SERPINA1 and CD59 as candidate biomarkers for COVID-19 severity stratification and prognosis prediction.

BACKGROUND: COVID-19 has been closely associated with coagulation abnormalities. However, existing biomarkers, including D-dimer and fibrin degradation products (FDP), exhibit limited accuracy in stratifying disease severity and predicting long-term clinical outcomes. OBJECTIVES: This study aimed to use proteomic analysis to identify plasma biomarkers associated with COVID-19 severity and prognosis, and validate their predictive utility for mortality and thromboembolic complications. METHODS: Plasma proteomic profiles were analyzed across three COVID-19 severity classes. Differential expression analysis and functional analysis were performed. Clustering analysis was used to identify proteins correlated with disease severity. Candidate biomarkers were validated in an independent cohort. Predictive performance of the biomarkers for mortality, sepsis and venous thromboembolism was evaluated using bootstrap-corrected ROC analyses and multivariable regression analyses. RESULTS: Proteomic analysis revealed progressive involvement of the coagulation and complement pathway with increasing disease severity. SERPINA1 and CD59 were identified as candidate biomarkers and exhibited significantly higher plasma levels in severe cases. Bootstrap-corrected ROC analyses demonstrated strong predictive performance: SERPINA1 achieved AUCs of 0.775 and 0.924 for 30-day and 12-month mortality, and CD59 achieved AUCs of 0.720 for sepsis; the combined model further improved prediction of 12-month mortality (AUC 0.946) and sepsis (AUC 0.904), outperforming D-dimer and FDP. Multivariable regression confirmed their independent prognostic value. CONCLUSION: This exploratory study identifies SERPINA1 and CD59 as candidate prognostic biomarkers in COVID-19, highlighting the role of coagulation and complement-related pathways in disease severity and warranting further prospective validation.

Humans↗

PubMed2026

Candidate biomarkers for early Giardia duodenalis infection revealed by time-resolved secretome proteomics.

Giardia duodenalis is a zoonotic protozoan parasite that causes giardiasis in humans and other mammals. Early diagnosis remains challenging because current diagnostic methods, including microscopy and enzyme-linked immunosorbent assays (ELISAs), primarily detect established infections. Consequently, a critical diagnostic gap exists during the early stage of infection within the first 2-48 h following exposure. To address this limitation, we characterized the proteins released by in vitro-cultured G. duodenalis trophozoites under serum-free conditions and evaluated their potential as early diagnostic biomarkers. Proteomic analysis of culture supernatants collected during early trophozoite incubation identified 31,773 peptides corresponding to 2504 quantifiable proteins. Temporal profiling showed distinct secretion patterns, including proteins that peaked during the early stage, progressively accumulated over time, or remained persistently abundant throughout the incubation period. Based on their secretion characteristics and predicted immunogenic properties, five candidate biomarkers were selected for further evaluation. Polyclonal antibodies raised against selected candidates successfully detected the corresponding proteins in serum-free culture supernatants, providing preliminary evidence for their potential utility as early-stage diagnostic targets. These findings identify stage-associated candidate proteins that may serve as a resource for future early giardiasis diagnostic development, provide a valuable resource for investigating host-parasite interactions, and establish a foundation for future diagnostic assay development. However, further validation in clinical and biological samples is required to confirm their diagnostic applicability. SIGNIFICANCE: Giardiasis, caused by Giardia duodenalis, is a major diarrheal disease worldwide. Although enzyme-linked immunosorbent assays (ELISAs) provide rapid detection, their diagnostic utility is limited by the lack of biomarkers capable of identifying infection during its earliest stages, creating a critical gap in the detection of active infection within 2-48 h following exposure. Using data-independent acquisition proteomics, this study provides a time-resolved characterization of proteins released by G. duodenalis trophozoites into serum-free culture supernatants. Our findings reveal temporal secretion dynamics of protein secretion and identify candidate biomarkers with potential utility for the development of early-stage diagnostic assays pending rigorous biological and clinical validation. In addition, this proteomic resource provides a foundation for investigating host-parasite interactions and may facilitate the development of future point-of-care diagnostic strategies.

Giardiasis↗

PubMed2026

Plasma proteome profiling identifies XPNPEP3 as a novel biomarker associated with metabolic dysfunction-associated steatotic liver disease in patients with type 2 diabetes mellitus.

OBJECTIVE: To identify plasma protein differences between type 2 diabetes mellitus (T2DM) patients with and without metabolic dysfunction-associated steatotic liver disease (MASLD), and to evaluate the diagnostic potential of X-prolyl aminopeptidase 3 (XPNPEP3) for identifying MASLD in T2DM patients. METHODS: Twenty T2DM inpatients were categorized into groups with and without MASLD and their plasma samples were analyzed using data-independent acquisition mass spectrometry, followed by bioinformatics analysis to identify differentially expressed proteins. The cohort was then expanded to 84 patients, and plasma XPNPEP3 levels were validated by enzyme-linked immunosorbent assay. Correlation between XPNPEP3 and clinical indicators were evaluated, and diagnostic performance was determined via receiver operating characteristic (ROC) analysis. Immunohistochemistry was employed to compare hepatic XPNPEP3 expression between the two groups. RESULTS: Proteomic analysis identified 176 differentially expressed proteins, with XPNPEP3 exhibiting the most significant down-regulation by fold change. In the validation cohort, plasma XPNPEP3 was significantly lower in T2DM+MASLD versus T2DM alone. XPNPEP3 levels were negatively correlated with diabetes duration, liver function markers, and triglyceride levels, and was identified as an independent factor inversely associated with MASLD in T2DM.ROC analysis demonstrated strong diagnostic performance for XPNPEP3, further enhanced when combined with BMI and diabetes duration.  Immunohistochemistry confirmed reduced hepatic XPNPEP3 expression in T2DM+MASLD patients. CONCLUSIONS: Lower plasma XPNPEP3 is independently associated with MASLD in T2DM patients and demonstrates strong diagnostic potential, positioning XPNPEP3 as a promising biomarker for diagnosing MASLD in T2DM patients and a novel target for non-invasive diagnostic tool development.

Humans↗

PubMed2026

Measurement of low-density lipoprotein cholesterol and other circulating lipids in Brazil: a systematic literature review.

Accurate laboratory assessment of circulating lipids underpins cardiovascular risk stratification, yet clinical interpretation depends not only on the assays but on the formula chosen to estimate low-density lipoprotein cholesterol (LDL-C). This review integrates the 2019-2025 evidence on laboratory methods for triglycerides (TG), total cholesterol (TC), and high-density lipoprotein cholesterol (HDLC), and on the formulas estimating LDL-C, VLDL-C, and non-HDL cholesterol, to determine how these should be measured, reported, and harmonized in Brazil, where lipid thresholds are adapted from international consensus. A PRISMA 2020 systematic search (PROSPERO CRD420251241064) of PubMed/MEDLINE, Scopus, SciELO, LILACS, Web of Science, and Embase retrieved 57,915 records; after removing 38,210 duplicates, 19,705 titles/abstracts were screened, 312 full texts assessed, and 25 sources included. Enzymatic colorimetric assays remain standard for TG, TC, and HDLC. For LDL-C, Martin/Hopkins classifies more accurately than Friedewald (89.6% vs 83.2% correct categorization in 5,051,467 patients), particularly at high TG and low LDL-C, while Sampson/NIH and modified Sampson/NIH extend reliable estimation into hypertriglyceridemia and very low LDL-C; direct measurement is reserved for TG beyond the validated range. Although the review centers on the Friedewald, Martin/Hopkins, and Sampson/NIH families that dominate guideline practice, other published equations exist and are addressed in context. In Brazil, atherogenic-lipid thresholds are risk-based decision limits rather than reference intervals; national surveys describe lipid distributions but were not designed to establish them. Analytical standardization through traceability programs, multicenter validation of formulas, and-where the distribution-based construct applies (HDLC, pediatrics)-nationally derived reference intervals are priorities for equitable cardiovascular risk assessment in Brazil.

Humans↗

PubMed2026

Assay-dependent variability in peptide biomarker quantification: experimental evidence from renalase in chronic kidney disease.

BACKGROUND: Renalase is a promising biomarker for kidney disease, but published levels vary widely between studies. We hypothesised that variability in commercial enzyme-linked immunosorbent assays (ELISAs) kits and matrix effects (serum vs plasma) drive these inconsistencies. METHODS: Paired serum and plasma samples from 56 participants (28 chronic kidney disease (CKD) stages 2-5, 28 healthy controls) were tested using three commercial renalase ELISAs (BTLAB, Cloud-Clone, EIAab). We assessed intra-assay precision, inter-assay agreement (Spearman's rank correlation and Bland-Altman analysis on log10-transformed values), matrix effects, and associations with estimated glomerular filtration rate (eGFR). Diagnostic performance was evaluated by Receiver operating characteristic (ROC) analysis. RESULTS: Inter-assay renalase concentrations differed markedly (up to orders of magnitude), with weak inter-assay correlations (r ≤ 0.25). Bland-Altman analyses revealed large, systematic biases between kits. Only the BTLAB assay showed consistent serum/plasma agreement, a significant correlation with eGFR (ρ ≈ 0.32-0.42, p < 0.05), and moderate discriminatory performance for CKD in serum (AUC = 0.70) and plasma (AUC = 0.68). Cloud-Clone and EIAab produced divergent results and strong matrix-dependent biases. CONCLUSIONS: Observed variability among commercial ELISA platforms may compromise comparability between studies. Harmonisation, standardised reference materials, and cross-validation are necessary before renalase assays can be used reliably in clinical practice.

Humans↗

PubMed2026

Dealcoholized muscadine wine improved skin elasticity and oxidative stress biomarkers without affecting gut microbiome in women over 40 in a randomized controlled trial.

Muscadine wine has a unique polyphenol profile distinct from that of common wine, and limited research exists on its health benefits. This study aimed to investigate the effects of intake of dealcoholized muscadine wine (DMW) on skin health, oxidative stress, inflammatory biomarkers, and the gut microbiome. Seventeen healthy women were randomly assigned to consume 300 mL of DMW or a placebo daily for 6 weeks, separated by a 3-week washout period, in a randomized, single-blinded, crossover design. Skin health parameters were measured on the face and forearm. Oxidative stress and inflammatory biomarkers were assessed in plasma. Fecal bacterial DNA was sequenced using shotgun sequencing. DMW did not affect UVB-induced erythema compared to placebo. However, it significantly decreased transepidermal water loss and increased facial gross elasticity. Skin elasticity significantly improved on the forearm, whereas other skin parameters were not affected. DMW significantly decreased plasma levels of matrix metalloproteinase-9 and advanced glycation end products compared with placebo. However, the abundance, diversity, and functions of the gut microbiome were not affected. Polyphenol-rich DMW administered for six weeks improved certain skin health parameters and reduced oxidative and inflammatory stress, without affecting the gut microbiome in healthy women.

Humans↗

PubMed2026

Insights from changes in NDEV biomarkers of metabolism: effects of PPARγ and GLP1 receptor agonists on brain metabolism.

BACKGROUND: Insulin resistance (IR) is implicated in central nervous system disorders, including depression and Alzheimer's disease (AD). METHODS: We analyzed biological samples from two cohorts of clinical trial participants: (1) participants with unremitted depression after six months of treatment as usual who received pioglitazone (PPARγ agonist, N = 12) or placebo and (2) middle-aged participants at genetic risk for AD who received liraglutide (glucagon-like peptide 1 [GLP1] receptor agonist, N = 15) or placebo. These cohorts, which previously showed treatment-related improvements in peripheral IR, were used to assess the effects of pioglitazone and liraglutide on CNS insulin signaling using neuron-derived extracellular vesicles (NDEVs) as biomarkers. We utilized biological samples to measure biomarkers of IR in NDEVs. Eleven Akt-mTOR pathway proteins were measured before and after 12 weeks of treatment in both groups. RESULTS: Participants who received pioglitazone experienced broader changes, with significant increases in GSK3β (Ser9), mTOR (Ser2448), and RPS6 (Ser235/Ser236; all P ≤ .02) compared with placebo, and 77% of participants showed mTOR (Ser2448) response. Participants who received liraglutide demonstrated significantly increased NDEV-associated phosphorylated Akt (Ser473) and mTOR (Ser2448; P = .04 and P = .025, respectively) compared with placebo, with 40% and 30% of participants in the liraglutide group showing biomarker response in both Akt (Ser473) and mTOR (Ser2448), respectively. These effects appeared relatively independent from changes in fasting plasma insulin and glucose concentration at 120-minutes during the oral glucose tolerance test. DISCUSSION: Our findings demonstrate CNS-specific biomarker responses to both PPARγ agonists and GLP1 receptor agonists.

Humans↗

PubMed2026

Translating single-cell RNA sequencing into monocyte direct leukocyte subpopulation-transcript abundance assay ratio-based biomarkers (IFI27/PSAP or IFI27/CTSS) for clinical detection of viral infection.

A rapid method for triaging febrile patients by aetiology (e.g., viral or bacterial infection) using gene expression in peripheral blood (PB) is an intensively researched area. However, gene expression in blood represents a composite sum of gene expression of all the component cell types present in the sample. As a result, numerous genes are measured in most proposed signatures. Herein, we propose a simple ratio-based biomarker (RBB) called direct leukocyte subpopulation-transcript abundance assay (DIRECT LS-TA) that recapitulates gene expressions of a single cell type in PB (i.e., monocytes). Based on single-cell RNA sequencing (scRNAseq) data and bulk expression data, IFI27 and SIGLEC1 are found as interferon-stimulated genes (ISGs) predominantly expressed by monocytes. The DIRECT LS-TA method can use a simple ratio of two genes measured in PB as an RBB to represent the target gene expression in monocytes without the need for monocyte purification. Both scRNAseq and bulk RNA sequencing datasets were used to evaluate the correlation between ISG expression in monocytes and PB, with a particular focus on monocyte expression of IFI27. An iceberg plot of bulk transcriptome data was used to identify genes that were predominantly expressed by monocytes in PB. DIRECT LS-TA RBBs of the three genes (IFI27, IFI44L and SIGLEC1) were evaluated by group-wise comparison, receiver operating characteristic and meta-analysis. In addition, the conventional interferon (IFN) score was evaluated for comparison of diagnostic performance. In viral infection datasets, DIRECT LS-TA of IFI27 (IFI27/PSAP or IFI27/CTSS) was most intensely activated (p value by t test <1e-9) and had the best area under the curve (0.94) among the three potential monocyte ISGs analysed. DIRECT LS-TA SIGLEC1 was also another monocyte biomarker but showed a lower activation (p<9e-5). IFI27/PSAP showed better diagnostic performance than the conventional IFN score. On the other hand, IFI44L was not a predominant monocyte expression gene. DIRECT LS-TA of IFI27 (IFI27/PSAP or IFI27/CTSS) measured in PB was the best biomarker of viral infection and IFN activation among ISGs predominantly expressed by monocytes. It performed even better than the conventional IFN score which required quantification of eight genes. The results suggest that DIRECT LS-TA of IFI27 is a monocyte-informative biomarker which is easy to determine in PB without the need for cell sorting.

Humans↗

PubMed2026

Integrated salivary proteomic and metabolomic analyses reveal molecular characterization and novel biomarker panels of chronic obstructive pulmonary disease.

Chronic obstructive pulmonary disease (COPD) is a respiratory disorder characterized by chronic inflammation, oxidative stress, and metabolic dysregulation. The lack of convenient and easily-accessible non-invasive diagnostic approaches remains a major clinical challenge. This study applied an integrated saliva-based proteomic and untargeted metabolomic strategy to identify potential biomarkers for COPD classification. Comprehensive multi-omics analyses identified 225 differentially abundant proteins and 60 differentially abundant metabolites between patients with COPD and healthy controls, including 24 biologically relevant endogenous metabolites. Functional enrichment analyses revealed pronounced dysregulation of mitochondrial energy metabolism, redox homeostasis, lipid remodeling, and inflammatory-related pathways in COPD. By integrating salivary proteomic and metabolomic biomarkers, a stepwise feature selection combined with LASSO logistic regression was used to construct diagnostic models, yielding an optimized biomarker panel consisting of 11 proteins and 2 endogenous metabolites. This integrated model achieved excellent diagnostic performance, with an area under the ROC curve of 0.96. Collectively, these findings demonstrate that integrated salivary proteomic and metabolomic profiling provides a robust, non-invasive approach for COPD classification and offers a promising foundation for the development of biosensor-based diagnostic platforms and early disease detection. SIGNIFICANCE: Chronic obstructive pulmonary disease (COPD) remains a major global health burden. Current diagnostic approaches rely largely on spirometry and clinical assessment, which are limited in sensitivity for early-stage disease and unsuitable for large-scale screening. This study employs an integrated saliva-based proteomic and metabolomic strategy to identify non-invasive biomarkers for COPD classification. Our findings reveal coordinated dysregulation of mitochondrial energy metabolism, redox homeostasis, and lipid remodeling in COPD, highlighting the interconnected roles of metabolic reprogramming, oxidative stress, and inflammation in disease pathophysiology. Notably, a robust diagnostic panel comprising 11 proteins and 2 endogenous metabolites was established, achieving excellent classification performance (AUC of 0.96). To our knowledge, the integrated application of salivary proteomics and metabolomics for COPD diagnosis remains largely unexplored, underscoring the significance and translational potential of our findings.

Humans↗

PubMed2026

Soluble CD163 as a Non-Invasive Biomarker in Autoimmune Nephrological and Rheumatological Diseases.

Autoimmune nephrological and rheumatological diseases involve macrophage-driven inflammation, yet disease activity is often assessed using invasive or non-specific measures. Soluble CD163 (sCD163), released from activated monocytes and macrophages, is emerging as a biomarker of macrophage-mediated inflammation in these conditions. This narrative review summarizes current evidence on the diagnostic, prognostic, and disease-monitoring potential of sCD163 measured in blood, urine, and synovial fluid in autoimmune nephrological and rheumatological diseases. This review is based on a narrative analysis of selected publications investigating the clinical utility of sCD163 in autoimmune kidney and rheumatic diseases, with emphasis on correlations with disease activity, histopathological findings, and clinical outcomes. Urinary sCD163 shows excellent diagnostic accuracy for active lupus nephritis (area under the receiver operating characteristic [AUROC] 0.89-0.998), correlates with histological activity index (but not chronicity), and distinguishes ongoing inflammation from chronic damage during treatment. In IgA nephropathy, it predicts remission failure and greater benefit from corticosteroids. In ANCA-associated vasculitis, it identifies active renal involvement (AUROC 0.95 in multicenter cohorts). In rheumatoid arthritis (RA), serum sCD163 correlates with early disease activity, predicts radiographic progression, and detects subclinical macrophage activation in remission. In spondylarthritis, synovial fluid sCD163 reflects a disease-specific M2-polarized macrophage phenotype distinct from RA. Utility is compartmentalized: urinary levels indicate intrarenal macrophage activation, synovial fluid local joint inflammation, and serum systemic activation. sCD163 is a promising macrophage-specific biomarker across autoimmune diseases, but its compartmentalized nature requires context-specific measurement. Before clinical implementation, assay standardization, multicenter validation, and interventional trials showing the benefit of sCD163-guided management are needed.

Humans↗

PubMed2026

Exploring sex differences in endocannabinoid system biomarkers and their relationship with antidepressant treatment outcomes in major depressive disorder: a CAN-BIND 1 secondary analysis.

BACKGROUND: Sex differences in major depressive disorder (MDD) are well documented, but it remains unclear whether sex-related variation in peripheral endocannabinoid system (ECS)-related biomarkers is detectable in MDD. OBJECTIVES: To examine baseline sex differences in ECS-related mRNA expression, DNA methylation, and single nucleotide polymorphisms (SNPs) in MDD, and associations between baseline ECS markers and antidepressant outcomes in sex-stratified analyses. METHODS: Among 178 participants with MDD from CAN-BIND-1, all received escitalopram for 8 weeks; non-responders then received adjunctive aripiprazole from Weeks 8-16.Response was defined as ≥ 50% reduction in MADRS score, and remission as MADRS ≤ 10. ANCOVAs examined baseline sex differences and sex-stratified biomarker associations with percent MADRS reduction at Weeks 8 and 16, as well as categorical response and remission outcomes. Covariates included site, baseline MADRS, age, and ethnicity. False discovery rate correction was applied. RESULTS: Baseline sex differences in methylation were observed for CACNA1H, GABRB2, MAGL, and GABRR2, though none survived correction. No baseline sex differences in mRNA expression or SNPs were detected after correction. Lower baseline DAGLA mRNA in males was associated with greater Week 8 symptom improvement (FDR corrected). This association was not observed in females. No associations with response or remission at Weeks 8 or 16 survived correction. IMPLICATIONS: Baseline sex differences in peripheral ECS-related markers were not detected in this sample. Larger studies are needed to verify whether ECS-related biomarkers, particularly DAGLA, contribute to antidepressant outcomes in a sex-specific manner.

Humans↗