BACKGROUND: Food intake biomarkers are used to estimate dietary exposure; however, selecting a single biomarker to evaluate a specific dietary component is difficult due to the overlap of diverse compounds from different foods. Therefore, combining two or more biomarkers can increase the sensitivity and specificity of food intake estimates.
OBJECTIVE: This study aimed to evaluate the ability of metabolite panels to distinguish between self-reported fruit consumers and non-consumers among participants in the Longitudinal Study of Adult Health.
METHODS: A total of 93 healthy adults of both sexes were selected from the Longitudinal Study of Adult Health. A 24-h dietary recall was obtained using the computer-assisted 24-h food recall GloboDiet software, and 24-h urine samples were collected from each participant. Metabolites were identified in urine using liquid chromatography coupled with high-resolution mass spectrometry by comparing their exact mass and fragmentation patterns using free-access databases. Multivariate receiver operating characteristic curve (ROC) analysis and partial least squares discriminant analysis were used to verify the ability of the metabolite combination to classify daily and non-daily fruit consumers. Fruit intake was identified using a 24 h dietary recall (24 h-DR).
RESULTS: Bananas, grapes, and oranges are included in the summary. The panel of biomarkers exhibited an area under the curve (AUC) > 0.6 (Orange AUC = 0.665; Grape AUC = 0.622; Bananas AUC = 0.602; All fruits AUC = 0.679; Citrus AUC = 0.693) and variable importance projection score > 1.0, and these were useful for assessing the sensitivity and predictability of food intake in our population.
CONCLUSIONS: A panel of metabolites was able to classify self-reported fruit consumers with strong predictive power and high specificity and sensitivity values except for banana and total fruit intake.

Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease characterized by inflammation in the synovial lining of the joints. Key inflammatory cytokines such as interleukin-6 (IL-6), TNF-α, and others play a critical role in the activation of local synovial leukocytes and the induction of chronic inflammation. Tocilizumab (TCZ), a humanized anti-IL-6 receptor monoclonal antibody, has demonstrated significant clinical efficacy in treating RA patients. However, similar to other inflammatory cytokine blockers, such as TNF-alpha inhibitors, Interleukin-1 inhibitors, or CD20 inhibitors, some patients do not respond to treatment. To address this challenge, our study employed a high-precision proteomics approach to identify protein biomarkers capable of predicting clinical responses to Tocilizumab in RA patients. Through the use of data-independent acquisition (DIA) mass spectrometry, we analyzed serum samples from both TCZ responders and non-responders to discover potential biomarker candidates. These candidates were subsequently validated using individual serum samples from two independent cohorts: a training set (N = 70) and a test set (N = 18), allowing for the development of a robust multi-biomarker panel. The constructed multi-biomarker panel demonstrated an average discriminative power of 86 % between response and non-response groups, with a high area under the curve (AUC) value of 0.84. Additionally, the panel exhibited 100 % sensitivity and 60 % specificity. Collectively, our multi-biomarker panel holds promise as a diagnostic tool to predict non-responders to TCZ treatment in RA patients.

Randomized clinical trials substantiate cannabidiol (CBD) as a next-generation antipsychotic, effective in alleviating positive and negative symptoms associated with psychosis, while minimising the adverse effects seen with established treatments. Although the mechanisms remain debated, CBD is known to induce drug-responsive changes in lipid-based retrograde neurotransmitters. Lipid aberrations are also frequently observed with antipsychotics, which may contribute to their efficacy or increase the risk of undesirables, including metabolic dysfunction, obesity and dyslipidaemia. Our study investigated CBD\'s impact following lipid responses triggered by interaction with second-generation antipsychotics (SGA) in a randomized phase I safety study. Untargeted mass spectrometry assessed the lipidomic profiles of human sera, collected from 38 healthy volunteers. Serum samples were obtained prior to commencement of any medication (t = 0), 3 days after consecutive administration of one of the five, placebo-controlled, treatment arms designed to achieve steady-state concentrations of each SGA (amisulpride, 150 mg/day; quetiapine, 300 mg/day; olanzapine 10 mg/day; risperidone, 3 mg/day), and after six successive days of SGA treatment combined with CBD (800 mg/day). Receiver operating characteristics (ROC) refined 3712 features to a putative list of 15 lipids significantly altered (AUC > 0.7), classified into sphingolipids (53 %), glycerolipids (27 %) and glycerophospholipids (20 %). Targeted mass spectrometry confirmed reduced sphingomyelin and ceramide levels with antipsychotics, which mapped along their catabolic pathway and were restored by CBD. These sphingolipids inversely correlated with body weight after olanzapine, quetiapine, and risperidone treatment, where CBD appears to have arrested or attenuated these effects. Herein, we propose CBD may alleviate aberrant sphingolipid metabolism and that further investigation into sphingolipids as markers for monitoring side effects of SGAs and efficacy of CBD is warranted.

BACKGROUND: To date, most existing models for predicting neuromyelitis optica spectrum disorder (NMOSD) are based primarily on clinical characteristics. Blood-based NMOSD severity and prognostic predictive immune- and inflammation-related biomarkers are needed. We aimed to investigate the associations between plasma inflammatory biomarkers and relapse and attack severity in NMOSD.
METHODS: This two-step, single-center prospective cohort study included discovery and validation cohorts. We quantified 92 plasma inflammatory proteins by using Olink\'s proximity extension assay and identified differentially expressed proteins in the relapse group (relapse within 1 year of follow-up) and severe attack group. To define a new molecular prognostic model, we calculated the risk score of each patient based on the key protein signatures and validated the results in the validation cohort.
RESULTS: The relapse prediction model, including FGF-23, DNER, GDNF, and SLAMF1, predicted the 1-year relapse risk. The severe attack prediction model, including PD-L1 and MCP-2, predicted the severe clinical attack risk. Both the relapse and severe attack prediction models demonstrated good discriminative ability and high accuracy in the validation cohort.
CONCLUSIONS: Our discovered biomarker signature and prediction models may complement current clinical risk stratification approaches. These inflammatory biomarkers could contribute to the discovery of therapeutic interventions and prevent NMOSD progression.

Inflammatory bowel disease (IBD) is a global health burden which carries lifelong morbidity affecting all age groups in populations with the disease-specific peak of the age groups ranging between 15 and 35 years, which are of great economic importance for the society. An accelerating incidence of IBD is reported for newly industrialised countries, whereas stabilising incidence but increasing prevalence is typical for countries with a Westernised lifestyle, such as the European area and the USA. Although the aetiology of IBD is largely unknown, the interplay between the genetic, environmental, immunological, and microbial components is decisive for the disease manifestation, course, severity and individual outcomes. Contextually, the creation of an individualised patient profile is crucial for the cost-effective disease management in primary and secondary care of IBD. The proposed pathomechanisms include intestinal pathoflora and dysbiosis, chronic inflammation and mitochondrial impairments, amongst others, which collectively may reveal individual molecular signatures defining IBD subtypes and leading to clinical phenotypes, patient stratification and cost-effective protection against health-to-disease transition and treatments tailored to individualised patient profiles-all the pillars of an advanced 3PM approach. The paradigm change from reactive medical services to predictive diagnostics, cost-effective targeted prevention and treatments tailored to individualised patient profiles in overall IBD management holds a promise to meet patient needs in primary and secondary care, to increase the life-quality of affected individuals and to improve health economy in the area of IBD management. This article analyses current achievements and provides the roadmap for future developments in the area in the context of 3P medicine benefiting society at large.

Crohn\'s disease (CD) and rheumatoid arthritis (RA) are immune-mediated inflammatory diseases. However, the molecular mechanisms linking these two diseases remain unclear.
To identify shared core genes between CD and RA, we employed differential gene analysis and the least absolute shrinkage and selection operator (LASSO) algorithm. Functional annotation of these core biomarkers was performed using consensus clustering and gene set enrichment analysis. We also constructed a protein-protein network and a miRNA-mRNA network using multiple databases, and potential therapeutic agents targeting the core biomarkers were predicted. Finally, we confirmed the expression of the genes in the biomarker panel in both CD and RA using quantitative PCR.
A total of five shared core genes, namely C-X-C motif chemokine ligand 10 (CXCL10), C-X-C motif chemokine ligand 9 (CXCL9), aquaporin 9 (AQP9), secreted phosphoprotein 1 (SPP1), and metallothionein 1M (MT1M), were identified as core biomarkers. These biomarkers activate classical pro-inflammatory and immune signaling pathways, influencing immune cell aggregation. Additionally, testosterone was identified as a potential therapeutic agent targeting the biomarkers identified in this study. The expression of genes in the biomarker panel in CD and RA was confirmed through quantitative PCR.
Our study revealed some core genes shared between CD and RA and established a novel biomarker panel with potential implications for the diagnosis and treatment of these diseases.

Background: Salivary epigenetic biomarkers may detect esophageal cancer. Methods: A total of 256 saliva samples from esophageal adenocarcinoma patients and matched volunteers were analyzed with Illumina EPIC methylation arrays. Three datasets were created, using 64% for discovery, 16% for testing and 20% for validation. Modules of gene-based methylation probes were created using weighted gene coexpression network analysis. Module significance to disease and gene importance to module were determined and a random forest classifier generated using best-scoring gene-related epigenetic probes. A cost-sensitive wrapper algorithm maximized cancer diagnosis. Results: Using age, sex and seven probes, esophageal adenocarcinoma was detected with area under the curve of 0.72 in discovery, 0.73 in testing and 0.75 in validation datasets. Cancer sensitivity was 88% with specificity of 31%. Conclusion: We have demonstrated a potentially clinically viable classifier of esophageal cancer based on saliva methylation.

BACKGROUND: Diagnostic panels based on multiple biomarkers and clinical characteristics are considered more favorable than individual biomarker to diagnose hepatocellular carcinoma (HCC). Based on age, sex, alpha-fetoprotein (AFP), and protein induced by vitamin K absence II (PIVKA-II) with/without AFP-L3, ASAP and GALAD models are potential diagnostic panels. The diagnostic performances of these two panels were compared relative to HCC detection among patients with various etiologies of chronic liver diseases (CLDs).
METHODS: A multicenter case-control study recruited CLDs patients with and without HCC from 14 Chinese hospitals. The etiologies of CLDs included hepatitis B virus (HBV), hepatitis C virus (HCV), alcoholic liver disease (ALD), and nonalcoholic fatty liver disease (NAFLD). Using area under the receiver operating characteristic curve (AUC) values, the diagnostic performances of ASAP and GALAD models were compared to detect HCC among patients with various etiologies of CLDs.
RESULTS: Among 248 HCC patients and 722 CLD controls, the ASAP model demonstrated the highest AUC (0.886) to detect HCC at any stage, outperforming the GALAD model (0.853, P = 0.001), as well as any individual biomarker (0.687-0.799, all P < 0.001). In the subgroup analysis of various CLDs etiologies, the ASAP model outperformed the GALAD model to HCC independent of CLDs etiology. In addition, the ASAP model performed better in detecting early-stage (BCLC stage 0/A) HCC versus the GALAD model.
CONCLUSIONS: Despite using one less laboratory variable (AFP-L3), the ASAP model demonstrated better diagnostic performance than the GALAD model to detect all-stage HCC among patients with various etiologies of CLDs-related HCC.

BACKGROUND: Prostate Cancer (PCa) represents the second leading cause of cancer-related death in men. Prostate-specific antigen (PSA) serum testing, currently used for PCa screening, lacks the necessary sensitivity and specificity. New non-invasive diagnostic tools able to discriminate tumoral from benign conditions and aggressive (AG-PCa) from indolent forms of PCa (NAG-PCa) are required to avoid unnecessary biopsies.
METHODS: In this work, 32 formerly N-glycosylated peptides were quantified by PRM (parallel reaction monitoring) in 163 serum samples (79 from PCa patients and 84 from individuals affected by benign prostatic hyperplasia (BPH)) in two technical replicates. These potential biomarker candidates were prioritized through a multi-stage biomarker discovery pipeline articulated in: discovery, LC-PRM assay development and verification phases. Because of the well-established involvement of glycoproteins in cancer development and progression, the proteomic analysis was focused on glycoproteins enriched by TiO2 (titanium dioxide) strategy.
RESULTS: Machine learning algorithms have been applied to the combined matrix comprising proteomic and clinical variables, resulting in a predictive model based on six proteomic variables (RNASE1, LAMP2, LUM, MASP1, NCAM1, GPLD1) and five clinical variables (prostate dimension, proPSA, free-PSA, total-PSA, free/total-PSA) able to distinguish PCa from BPH with an area under the Receiver Operating Characteristic (ROC) curve of 0.93. This model outperformed PSA alone which, on the same sample set, was able to discriminate PCa from BPH with an AUC of 0.79. To improve the clinical managing of PCa patients, an explorative small-scale analysis (79 samples) aimed at distinguishing AG-PCa from NAG-PCa was conducted. A predictor of PCa aggressiveness based on the combination of 7 proteomic variables (FCN3, LGALS3BP, AZU1, C6, LAMB1, CHL1, POSTN) and proPSA was developed (AUC of 0.69).
CONCLUSIONS: To address the impelling need of more sensitive and specific serum diagnostic tests, a predictive model combining proteomic and clinical variables was developed. A preliminary evaluation to build a new tool able to discriminate aggressive presentations of PCa from tumors with benign behavior was exploited. This predictor displayed moderate performances, but no conclusions can be drawn due to the limited number of the sample cohort. Data are available via ProteomeXchange with identifier PXD035935.

OBJECTIVE: To study serum quantities of neuron specific enolase (NSE), glial fibrillary acidic protein (GFAP) and NR2-antibodies (NR2-ab) in various cerebrovascular pathology and assess their value as a panel used as a diagnostic and predictive tool for stroke.
METHODS: NSE, GFAP and NR2-ab serum levels were measured twice for 84 patients with ischemic stroke (IS) and 8 patients with hemorrhagic stroke (HI), once for 8 patients with transient ischemic attack (TIA), 26 patients with chronic brain ischemia (CBI), 27 healthy volunteers (HV).
RESULTS: NSE and GFAP levels were significantly higher in IS than in CBI and HV patients, and NR2-ab levels in IS were higher than in TIA and lower than in HV. In patients with more pronounced neurological deficiency and less favorable functional outcome by day 10-14 of IS, the levels of NSE, GFAP and NR2-ab were higher. Sensitivity and specificity of biomarker panel was higher than with their separate application.
CONCLUSIONS: The NSE, GFAP and NR2-ab biomarkers have a diagnostic and predictive value for IS.
UNASSIGNED: Изучить значения нейронспецифической енолазы (НСЕ), глиального фибриллярного кислого белка (ГФКБ) и NR2-антител (NR2-АТ) в сыворотке крови (СК) при цереброваскулярной патологии и оценить их ценность как панели в качестве диагностического и прогностического инструмента при инсульте.
UNASSIGNED: Уровни НСЕ, ГФКБ и NR2-АТ измеряли в крови дважды у 84 пациентов с ишемическим инсультом (ИИ) в динамике и у 8 — с геморрагическим инсультом (ГИ), однократно у 8 пациентов с транзиторной ишемической атакой (ТИА), у 26 — с хронической ишемией мозга (ХИМ), у 27 здоровых добровольцев (ЗД).
UNASSIGNED: Уровни НСЕ и ГФКБ были значимо выше при ИИ, чем у пациентов с ХИМ и у ЗД, а уровни NR2-АТ при ИИ были выше, чем при ТИА, и ниже, чем у ЗД. У пациентов с более выраженным неврологическим дефицитом и менее благоприятным исходом к 10—14-му дню ИИ уровни НСЕ, ГФКБ и NR2-АТ были выше. Чувствительность и специфичность биомаркеров в составе панели были выше, чем при применении их по отдельности.
UNASSIGNED: Панель биомаркеров НСЕ, ГФКБ и NR2-АТ обладает диагностической и прогностической ценностью при ИИ.