Biomarker panels for fruit intake assessment: a metabolomics analysis in the ELSA-Brasil study.

INTRODUCTION: 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. MATERIALS AND 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. CONCLUSION: 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.

Development of a predictive model to distinguish prostate cancer from benign prostatic hyperplasia by integrating serum glycoproteomics and clinical variables.

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.

Serum biomarker panel for disease severity and prognosis in patients with COVID-19.

BACKGROUND: Coronavirus disease-2019 (COVID-19) has become a worldwide emergency and has had a severe impact on human health. Inflammatory factors have the potential to either enhance the efficiency of host immune responses or damage the host organs with immune overreaction in COVID-19. Therefore, there is an urgent need to investigate the functions of inflammatory factors and serum markers that participate in disease progression. METHODS: In total, 54 COVID-19 patients were enrolled in this study. Disease severity was evaluated by clinical evaluation, laboratory tests, and computed tomography (CT) scans. Data were collected at: admission, 3-5 days after admission, when severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA detection became negative, and composite endpoint. RESULTS: We found that the positive rate in sputum was three times higher than that in throat swabs. Higher levels of C-reactive protein (CRP), lactate dehydrogenase (LDH), D-dimer (D-D), interleukin-6 (IL-6) and neutrophil-to-lymphocyte ratio (NLR) or lower lymphocyte counts suggested more severe disease, and the levels of cytokines and serum markers were intrinsically correlated with disease progression. When SARS-CoV-2 RNA detection became negative, the receiver operating characteristic (ROC) curve demonstrated that LDH had the highest sensitivity independently, and four indicators (NLR, CRP, LDH, and D-D) when combined had the highest sensitivity in distinguishing critically ill patients from mild ones. CONCLUSIONS: Monitoring dynamic changes in NLR, CRP, LDH, IL-6, and D-D levels, combined with CT imaging and viral RNA detection in sputum, could aid in severity evaluation and prognosis prediction and facilitate COVID-19 treatment.

Comparison between models for detecting hepatocellular carcinoma in patients with chronic liver diseases of various etiologies: ASAP score versus GALAD score.

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.

Exosomal miRNA Biomarker Panel for Pancreatic Ductal Adenocarcinoma Detection in Patient Plasma: A Pilot Study.

Pancreatic ductal adenocarcinoma (PDAC) is rapidly becoming one of the leading causes of cancer-related deaths in the United States, and with its high mortality rate, there is a pressing need to develop sensitive and robust methods for detection. Exosomal biomarker panels provide a promising avenue for PDAC screening since exosomes are highly stable and easily harvested from body fluids. PDAC-associated miRNAs packaged within these exosomes could be used as diagnostic markers. We analyzed a series of 18 candidate miRNAs via RT-qPCR to identify the differentially expressed miRNAs (p < 0.05, t-test) between plasma exosomes harvested from PDAC patients and control patients. From this analysis, we propose a four-marker panel consisting of miR-93-5p, miR-339-3p, miR-425-5p, and miR-425-3p with an area under the curve (AUC) of the receiver operator characteristic curve (ROC) of 0.885 with a sensitivity of 80% and a specificity of 94.7%, which is comparable to the CA19-9 standard PDAC marker diagnostic.

Modeling and integration of N-glycan biomarkers in a comprehensive biomarker data model.

Molecular biomarkers measure discrete components of biological processes that can contribute to disorders when impaired. Great interest exists in discovering early cancer biomarkers to improve outcomes. Biomarkers represented in a standardized data model, integrated with multi-omics data, may improve the understanding and use of novel biomarkers such as glycans and glycoconjugates. Among altered components in tumorigenesis, N-glycans exhibit substantial biomarker potential, when analyzed with their protein carriers. However, such data are distributed across publications and databases of diverse formats, which hamper their use in research and clinical application. Mass spectrometry measures of 50 N-glycans on 7 serum proteins in liver disease were integrated (as a panel) into a cancer biomarker data model, providing a unique identifier, standard nomenclature, links to glycan resources, and accession and ontology annotations to standard protein, gene, disease, and biomarker information. Data provenance was documented with a standardized United States Food and Drug Administration-supported BioCompute Object. Using the biomarker data model allows the capture of granular information, such as glycans with different levels of abundance in cirrhosis, hepatocellular carcinoma, and transplant groups. Such representation in a standardized data model harmonizes glycomics data in a unified framework, making glycan-protein biomarker data exploration more available to investigators and to other data resources. The biomarker data model we describe can be used by researchers to describe their novel glycan and glycoconjugate biomarkers; it can integrate N-glycan biomarker data with multi-source biomedical data and can foster discovery and insight within a unified data framework for glycan biomarker representation, thereby making the data FAIR (Findable, Accessible, Interoperable, Reusable) (https://www.go-fair.org/fair-principles/).

Diagnostic efficacy of circular RNAs as noninvasive, liquid biopsy biomarkers for early detection of gastric cancer.

BACKGROUND: Majority of gastric cancers (GC) are diagnosed at advanced stages which contributes towards their poor prognosis. In view of this clinical challenge, identification of non-invasive biomarker for early diagnosis is imperative. Herein, we aimed to develop a non-invasive, liquid-biopsy based assay by using circular RNAs (circRNAs) as molecular biomarkers for early detection of GC. METHODS: We performed systematic biomarker discovery and validation of the candidate circRNAs in matched tissue specimens of GC and adjacent normal mucosa. Next, we translated the discovered circRNA based biomarker panel into serum samples in a training and validation cohort of GC patients (n = 194) and non-disease controls (n = 94) and evaluated their diagnostic performance. In addition, we measured the expression of circRNAs in serum samples of pre- and post-surgical GC patients and evaluated the specificity of circRNAs biomarker panel with respect to other gastro-intestinal (GI) malignancies. RESULTS: We identified 10-circRNAs in the discovery phase with subsequent validation in a pilot cohort of GC tissue specimens. Using a training cohort of patients, we developed an 8-circRNA based risk-prediction model for the diagnosis of GC. We observed that our biomarker panel robustly discriminated GC patients from non-disease controls with an AUC of 0.87 in the training, and AUC of 0.83 in the validation cohort. Notably, the biomarker panel could robustly identify even early-stage GC patients, regardless of their tumor histology (diffuse vs. intestinal). The decreased expression of circRNAs in post-surgery serum specimens indicated their tumor-specificity and their potential source of origin in the systemic circulation. CONCLUSIONS: We identified a panel of 8-circRNAs as non-invasive, liquid-biopsy biomarkers which might serve as potential diagnostic biomarkers for the early detection of GC.

A liquid biopsy signature of circulating exosome-derived mRNAs, miRNAs and lncRNAs predict therapeutic efficacy to neoadjuvant chemotherapy in patients with advanced gastric cancer.

At present, there is no validated marker to identify the subpopulation of patients with advanced gastric cancer (AGC) who might benefit from neoadjuvant chemotherapy (NACT). In view of this clinical challenge, the identification of non-invasive biomarkers for efficacy prediction of NACT in patients with AGC is imperative. Herein, we aimed to develop a non-invasive, liquid-biopsy-based assay by using an exosome-derived RNAs model based on multi-omics characteristics of RNAs. We firstly used a multi-omics strategy to characterize the mRNAs, microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) profiles of circulating exosome enriched fractions in responders to NACT paired with non-responders, using RNA sequencing. Finally, numerous miRNAs, mRNAs and lncRNAs were identified to be associated with the response to NACT in patients with AGC, and it was validated in an independent cohort with promising AUC values. Furthermore, we established a 6-exosome-RNA panel that could robustly identified responders from non-responders treated with fluorouracil-based neoadjuvant chemotherapy.

Serum proteomics unveil characteristic protein diagnostic biomarkers and signaling pathways in patients with esophageal squamous cell carcinoma.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is a common digestive tract malignant tumor with high incidence and dismal prognosis worldwide. However, the reliable biomarkers for clinical diagnosis and the underlying signaling pathways insights of ESCC are not unequivocally understood yet. The serum proteome may provide valuable clues for the early diagnosis of ESCC and the discovery of novel molecular insights. METHODS: In the current study, an optimized proteomics approach was employed to discover novel serum-based biomarkers for ESCC, and unveil abnormal signal pathways. Gene ontology (GO) enrichment analysis was done by Gene Set Enrichment Analysis (GSEA) and Metascape database, respectively. Pathway analysis was accomplished by GeneCards database. The correlation coefficient was assessed using Pearson and distance correlation analyses. Prioritized candidates were further verified in two independent validation sets by enzyme-linked immunosorbent assay (ELISA) and immunohistochemistry (IHC) staining. RESULTS: A total of 633 non-redundant proteins were identified in the serum of patients with ESCC, of which 59 and 10 proteins displayed a more than 1.5-fold increase or decrease compared with healthy controls. Verification was performed for six candidate biomarkers, including S100A8/A9, SAA1, ENO1, TPI1 and PGAM1. Receiver operating characteristics (ROC) curve plotting showed the high diagnostic sensitivity and specificity of these six protein molecules as a biomarker panel: the area under characteristic curve (AUC) is up to 0.945. Differentially expressed proteins were subjected to functional enrichment analysis, which revealed the dysregulation of signaling pathways mainly involved in glycolysis, TLR4, HIF-1α, Cori cycle, TCA cycle, folate metabolism, and platelet degranulation. The latter finding was all the more noteworthy as a strong positive correlation was discovered between activated glycolysis and TLR4 pathways and unfavorable clinicopathological TNM stages in ESCC. CONCLUSIONS: Our findings propose a potential serum biomarker panel for the early detection and diagnosis of ESCC, which could potentially broaden insights into the characteristics of ESCC from the proteomic perspective.

A metabolic biomarker panel of restless legs syndrome in peritoneal dialysis patients.

BACKGROUND: Restless legs syndrome (RLS) is a neuromotor disorder, and dialysis patients are more likely to develop RLS. RLS often causes sleep disorders, anxiety and depression in patients. It will increase the risk of death and severely affect the life of patients. At present, RLS has not received enough recognition and attention, and the misdiagnosis rate can reach more than 10%. METHODS: The discovery set selected 30 peritoneal dialysis (PD) patients and 27 peritoneal dialysis patients with RLS (PD-RLS). A metabolomics method based on ultra performance liquid chromatography tandem quadrupole time-of-flight mass spectrometric method (UPLC-Q-TOF/MS) was used to analyze the differential metabolites of the two groups. 51 PD patients and 51 PD-RLS patients were included in the validation set. The receiver operating characteristic (ROC) analysis was used to evaluate the early diagnostic biomarkers, and the correlation between the differential metabolites and laboratory test indexes was analyzed to explore the biological function of the differential metabolites. RESULTS: Through the integrated analysis, four metabolites can be used as markers for the diagnosis of PD-RLS, including Hippuric acid, Phenylacetylglutamine, N,N,N-Trimethyl-L-alanyl-L-proline betaine and Threonic acid. Through ROC analysis, it is found that they can be used as a metabolic biomarker panel, and the area under the curve of this combination is more than 0.9, indicating that the panel has good diagnostic and predictive ability. CONCLUSION: Metabolomics based on UPLC-Q-TOF/MS technology can effectively identify the potential biomarkers, and provide a theoretical basis for the early diagnosis, prevention and treatment on PD-RLS.

Utilising lipid and, arginine and proline metabolism in blood plasma to differentiate the biochemical expression in functional dyspepsia (FD) and irritable bowel syndrome (IBS).

Functional gastrointestinal disorders (FGID) such as functional dyspepsia (FD) and irritable bowel syndrome (IBS) are highly prevalent and debilitating attributed to altered gut function and gut-brain interactions. FGID can be reliably diagnosed based upon the symptom pattern; but in the clinical setting FD or IBS a frequent diagnoses of exclusion after relevant structural causes of symptoms have been ruled out by appropriate testing. Thus far, there is no established biomarker for FGIDs. To address this limitation, we utilised multi-omics and chemometrics integration to characterise the blood plasma biochemistry in patients with IBS, FD, an overlap of FD/IBS, and controls using liquid chromatography-mass spectrometry (LC-MS) techniques.Cholesterol metabolism products Cholest-5,24-dien-3ß-ol, 3-O-ß-D-glucopyranoside, energy pathway metabolites, immunoglobulin-γ2 and immunoglobulin-κ, and carbonic anhydrase-1 proteins were particularly elevated in IBS. Furthermore, arginine and proline metabolisms, thyroid hormone synthesis, ferroptosis and, complementary and coagulation cascades were particularly upregulated in patients with IBS. Cer(d18:1/26:1(17Z)) and PI(14:0/22:1(11Z)) lipids were elevated in FD and FD-IBS but were depleted in IBS. Markers of central carbon metabolism and lipidome profiles allowed better discrimination and model predictability than metaproteome profile in healthy and FGID conditions.Overall, the multi-omics integration allowed the discrimination of healthy controls and FGID patients. It also effectively differentiated the biochemistry of FGID subtypes including FD, IBS and FD-IBS co-occurrence. This study points towards the possibility of multi-omics integration for rapid and high throughput analysis of plasma samples to support clinicians screen and diagnose patients with suspected FGIDs.

Combinatorial panel with endophenotypes from multilevel information of diffusion tensor imaging and lipid profile as predictors for depression.

OBJECTIVE: Clinical heterogeneity in major depressive disorder likely reflects the range of etiology and contributing factors in the disorder, such as genetic risk. Identification of more refined subgroups based on biomarkers such as white matter integrity and lipid-related metabolites could facilitate precision medicine in major depressive disorder. METHODS: A total of 148 participants (15 genetic high-risk participants, 57 patients with first-episode major depressive disorder and 76 healthy controls) underwent diffusion tensor imaging and plasma lipid profiling. Alterations in white matter integrity and lipid metabolites were identified in genetic high-risk participants and patients with first-episode major depressive disorder. Then, shared alterations between genetic high-risk and first-episode major depressive disorder were used to develop an imaging x metabolite diagnostic panel for genetically based major depressive disorder via factor analysis and logistic regression. A fivefold cross-validation test was performed to evaluate the diagnostic panel. RESULTS: Alterations of white matter integrity in corona radiata, superior longitudinal fasciculus and the body of corpus callosum and dysregulated unsaturated fatty acid metabolism were identified in both genetic high-risk participants and patients with first-episode major depressive disorder. An imaging x metabolite diagnostic panel, consisting of measures for white matter integrity and unsaturated fatty acid metabolism, was identified that achieved an area under the receiver operating characteristic curve of 0.86 and had a significantly higher diagnostic performance than that using either measure alone. And cross-validation confirmed the adequate reliability and accuracy of the diagnostic panel. CONCLUSION: Combining white matter integrity in corpus callosum, superior longitudinal fasciculus and corona radiata, and unsaturated fatty acid profile may improve the identification of genetically based endophenotypes in major depressive disorder to advance precision medicine strategies.

Large-scale plasma proteomic profiling identifies a high-performance biomarker panel for Alzheimer's disease screening and staging.

INTRODUCTION: Blood proteins are emerging as candidate biomarkers for Alzheimer's disease (AD). We systematically profiled the plasma proteome to identify novel AD blood biomarkers and develop a high-performance, blood-based test for AD. METHODS: We quantified 1160 plasma proteins in a Hong Kong Chinese cohort by high-throughput proximity extension assay and validated the results in an independent cohort. In subgroup analyses, plasma biomarkers for amyloid, tau, phosphorylated tau, and neurodegeneration were used as endophenotypes of AD. RESULTS: We identified 429 proteins that were dysregulated in AD plasma. We selected 19 "hub proteins" representative of the AD plasma protein profile, which formed the basis of a scoring system that accurately classified clinical AD (area under the curve  = 0.9690-0.9816) and associated endophenotypes. Moreover, specific hub proteins exhibit disease stage-dependent dysregulation, which can delineate AD stages. DISCUSSION: This study comprehensively profiled the AD plasma proteome and serves as a foundation for a high-performance, blood-based test for clinical AD screening and staging.

Noxa upregulation and 5-gene apoptotic biomarker panel in colorectal cancer.

BACKGROUND: NOXA and MCL1 are involved in the intrinsic pathway of apoptosis, where Noxa selectively binds to MCL1 and prevents it from inhibiting apoptosis. Both factors are considered as potential tumour biomarkers, while MCL1 has attracted interest as target in cancer. The purpose of this study was to investigate the expression of NOXA and MCL1 in 160 CRC tumour samples, to investigate their significance, also in combination with IAPs, DR5 expression and KRAS gene mutations in CRC. MATERIALS AND METHODS: Fresh frozen colorectal tissue was obtained from patients undergoing surgery for CRC. Real-time quantitative PCR was performed for the determination of mRNA expression levels. Protein expression was determined immunohistochemically. Differences in the mRNA expression profile were evaluated with the nonparametric Wilcoxon signed ranks test. Statistical analysis was performed with the use of Mann-Whitney U test and receiver-operating characteristic (ROC) curve. RESULTS: NOXA was found to be overexpressed in CRC tumours (P < .0001), even from early stage. Moreover, NOXA/MCL1 mRNA expression was significantly elevated in tumour samples compared to normal pairs (P < .0001). ROC curve analysis showed that both NOXA expression and its combination with Mcl1 expression have fair discriminatory value between CRC and normal colorectal tissue. Combinatorial ROC analysis revealed the most significant discriminatory value of NOXA, MCL1 with cIAP1 and cIAP2 (AUC = 0.834, P < .0001) as a 5-gene panel of markers. CONCLUSION: Noxa, Mcl1, DR5, cIAP1 and cIAP2 mRNA expressions are significantly deregulated in CRC and could provide a panel of markers with significant discriminatory value between CRC and normal colorectal tissue.

Hybrid design evaluating new biomarkers when there is an existing screening test.

Development of cancer screening biomarkers usually follows the Early Detection Research Network 5-Phase guideline in Pepe et al. A key feature of this guide is that the phased development follows a sequential order, moving to the next phase only when the current phase study is complete and has met its target performance. Motivated by a newly funded Newly onset Diabetes cohort study, we propose a design evaluating new biomarkers to discriminate between cases and controls in the presence of an existing screening test. The proposed design achieves two goals: (1) avoiding bias in estimating sensitivity or specificity in predicting cancer at a given time period prior to clinical diagnosis, using data from both screening detected cancers in Phase IV study and clinically diagnosed cancers in Phase III study; and (2) building a panel with biomarkers for Phase III and IV studies based on all data. A simulation study shows that the proposed design outperforms both a conventional method using data in Phase III arm only and a naive method using data in Phase III and IV arms ignoring the difference between the time of screening the detected cancer and the time of clinical diagnosis. The proposed design yields a smaller standard error of the estimation and increases the statistical power to confirm biomarker performance. This proposed method has the potential to shorten the cancer screening biomarker development process, use resources more effectively, and bring benefits to patients quickly.

The Enhanced Liver Fibrosis test maintains its diagnostic and prognostic performance in alcohol-related liver disease: a cohort study.

BACKGROUND: Alcohol is the main cause of chronic liver disease. The Enhanced Liver Fibrosis (ELF) test is a serological biomarker for fibrosis staging in chronic liver disease, however its utility in alcohol-related liver disease warrants further validation. We assessed the diagnostic and prognostic performance of ELF in alcohol-related liver disease. METHODS: Observational cohort study assessing paired ELF and histology from 786 tertiary care patients with chronic liver disease due to alcohol (n = 81) and non-alcohol aetiologies (n = 705). Prognostic data were available for 64 alcohol patients for a median of 6.4 years. Multiple ELF cut-offs were assessed to determine diagnostic utility in moderate fibrosis and cirrhosis. Survival data were assessed to determine the ability of ELF to predict liver related events and all-cause mortality. RESULTS: ELF identified cirrhosis and moderate fibrosis in alcohol-related liver disease independently of aminotransferase levels with areas under receiver operating characteristic curves of 0.895 (95% CI 0.823-0.968) and 0.923 (95% CI 0.866-0.981) respectively, which were non-inferior to non-alcohol aetiologies. The overall performance of ELF was assessed using the Obuchowski method: in alcohol = 0.934 (95% CI 0.908-0.960); non-alcohol = 0.907 (95% CI 0.895-0.919). Using ELF < 9.8 to exclude and ≧ 10.5 to diagnose cirrhosis, 87.7% of alcohol cases could have avoided biopsy, with sensitivity of 91% and specificity of 85%. A one-unit increase in ELF was associated with a 2.6 (95% CI 1.55-4.31, p < 0.001) fold greater odds of cirrhosis at baseline and 2.0-fold greater risk of a liver related event within 6 years (95% CI 1.39-2.99, p < 0.001). CONCLUSIONS: ELF accurately stages liver fibrosis independently of transaminase elevations as a marker of inflammation and has superior prognostic performance to biopsy in alcohol-related liver disease.

Blood-derived molecular signatures as biomarker panels for the early detection of colorectal cancer.

Colorectal cancer (CRC) is one of the leading causes of tumor morbidity and mortality worldwide. Endoscopy is currently the main screening method, but the invasiveness and high cost hamper the application of endoscopy in asymptomatic patients with a risk of CRC and lead to a low diagnostic rate for early CRC. In recent years, the progress of transcriptomics, epigenetics, immunomics and metabolomics has greatly contributed to the identification of novel molecular markers for the noninvasive screening of CRC, and many molecules in various biological processes have been identified and evaluated for CRC detection. However, individual molecules always have insufficient diagnostic performance as biomarkers for the detection of CRC; therefore, a frequent strategy to overcome this deficiency is the use of molecule signatures as biomarker panels to improve the diagnostic power. Here, we reviewed the diagnostic performance of blood-derived molecular signatures (mRNAs, microRNAs, autoantibodies, and metabolites) as biomarker panels for CRC detection, particularly for early detection, and discussed their limitations and prospects.

Gender Differences in Developing Biomarker-Based Major Depressive Disorder Diagnostics.

The identification of biomarkers associated with major depressive disorder (MDD) holds great promise to develop an objective laboratory test. However, current biomarkers lack discriminative power due to the complex biological background, and not much is known about the influence of potential modifiers such as gender. We first performed a cross-sectional study on the discriminative power of biomarkers for MDD by investigating gender differences in biomarker levels. Out of 28 biomarkers, 21 biomarkers were significantly different between genders. Second, a novel statistical approach was applied to investigate the effect of gender on MDD disease classification using a panel of biomarkers. Eleven biomarkers were identified in men and eight in women, three of which were active in both genders. Gender stratification caused a (non-significant) increase of Area Under Curve (AUC) for men (AUC = 0.806) and women (AUC = 0.807) compared to non-stratification (AUC = 0.739). In conclusion, we have shown that there are differences in biomarker levels between men and women which may impact accurate disease classification of MDD when gender is not taken into account.

A Markov Multi-State model of lupus nephritis urine biomarker panel dynamics in children: Predicting changes in disease activity.

BACKGROUND: A urine 'biomarker panel' comprising alpha-1-acid-glycoprotein, ceruloplasmin, transferrin and lipocalin-like-prostaglandin-D synthase performs to an 'excellent' level for lupus nephritis identification in children cross-sectionally. The aim of this study was to assess if this biomarker panel predicts lupus nephritis flare/remission longitudinally. METHODS: The novel urinary biomarker panel was quantified by enzyme linked immunoabsorbant assay in participants of the United Kingdom Juvenile Systemic Lupus Erythematosus (UK JSLE) Cohort Study, the Einstein Lupus Cohort, and the South African Paediatric Lupus Cohort. Monocyte chemoattractant protein-1 and vascular cell adhesion molecule-1 were also quantified in view of evidence from other longitudinal studies. Serial urine samples were collected during routine care with detailed clinical and demographic data. A Markov Multi-State model of state transitions was fitted, with predictive clinical/biomarker factors assessed by a corrected Akaike Information Criterion (AICc) score (the better the model, the lower the AICc score). RESULTS: The study included 184 longitudinal observations from 80 patients. The homogeneous multi-state Markov model of lupus nephritis activity AICc score was 147.85. Alpha-1-acid-glycoprotein and ceruloplasmin were identified to be the best predictive factors, reducing the AICc score to 139.81 and 141.40 respectively. Ceruloplasmin was associated with the active-to-inactive transition (hazard ratio 0.60 (95% confidence interval [0.39, 0.93])), and alpha-1-acid-glycoprotein with the inactive-to-active transition (hazard ratio 1.49 (95% confidence interval [1.10, 2.02])). Inputting individual alpha-1-acid-glycoprotein/ceruloplasmin values provides 3, 6 and 12 months probabilities of state transition. CONCLUSIONS: Alpha-1-acid-glycoprotein was predictive of active lupus nephritis flare, whereas ceruloplasmin was predictive of remission. The Markov state-space model warrants testing in a prospective clinical trial of lupus nephritis biomarker led monitoring.

Biomarkers for major depressive and bipolar disorders using metabolomics: A systematic review.

Major depressive disorder (MDD) and bipolar disorder (BD) lack robust biomarkers useful for screening purposes in a clinical setting. A systematic review of the literature was conducted on metabolomic studies of patients with MDD or BD through the use of analytical platforms such as in vivo brain imaging, mass spectrometry, and nuclear magnetic resonance. Our search identified a total of 7,590 articles, of which 266 articles remained for full-text revision. Overall, 249 metabolites were found to be dysregulated with 122 of these metabolites being reported in two or more of the studies included. A list of biomarkers for MDD and BD established from metabolites found to be abnormal, along with the number of studies supporting each metabolite and a comparison of which biological fluids they were reported in, is provided. Metabolic pathways that may be important in the pathophysiology of MDD and BD were identified and predominantly center on glutamatergic metabolism, energy metabolism, and neurotransmission. Using online drug registries, we also illustrate how metabolomics can facilitate the discovery of novel candidate drug targets.