Combinations Of Biomarkers For Diagnosis Research Articles

Complementary combination of biomarkers for diagnosis of sarcopenia in C57BL/6J mice

AimsThe objective of this study is to provide a reliable strategy for the diagnosis of sarcopenia based on a complementary combination of biomarkers from various approaches. Material and methodsA total of 30 C57BL/6J mice were used for the experiment, in which 15 young mice (YM) at 24 weeks old and 15 aged mice (AM) at 88 weeks old. Extracted features-based digital biomarkers from the electromyography activity of tibialis anterior muscles were evaluated by using receiver operating characteristic analysis. Extracted tissular proteins and circulating hormones based chemical biomarkers were investigated by using immunoblotting and enzyme-linked immunosorbent assay. Key findingsIn terms of digital biomarkers, the feature-based classification of mice groups showed good performance (Feature A: AUC = 0.986, accuracy = 0.928) and (Feature B: AUC = 0.999, accuracy = 0.990). On the other hand, muscle-specific protein levels based chemical biomarkers (e.g. MuRF1, FoxO1, and perilipin2) were observed significantly increase with age. Pro-inflammatory cytokines based biomarkers extracted from muscle tissue and circulating plasma (e.g. TNF-α, IL-6, and IL-8) were significantly higher in case of AM group compared to YM group. Circulating hormone-based chemical biomarkers (e.g. cortisol/DHEA ratio and cathepsin D) presented a significant increase in concentrations with age. Circulating neurotransmitter based biomarkers (e.g. acetylcholine, serotonin, and histamine) also increased significantly in concentrations from YM to AM. SignificanceA complementary combination of digital and chemical biomarkers covers multiple domains of sarcopenia to provide an effective strategy for the early diagnosis of sarcopenia.

Complementary Combination of Biomarkers for Diagnosis of Sarcopenia in C57BL/6J Mice

Complementary Combination of Biomarkers for Diagnosis of Sarcopenia in C57BL/6J Mice

Optimal combination of early biomarkers for infection and sepsis diagnosis in the emergency department: The BIPS study

Objective: To define the best combination of biomarkers for the diagnosis of infection and sepsis in the emergency room. Methods: In this prospective study, consecutive patients with a suspicion of infection in the emergency room were included. Eighteen different biomarkers measured in plasma, and twelve biomarkers measured on monocytes, neutrophils, B and T-lymphocytes were studied and the best combinations determined by a gradient tree boosting approach. Results: Overall, 291 patients were included and analysed, 148 with bacterial infection, and 47 with viral infection. The best biomarker combination which first allowed the diagnosis of bacterial infection, included HLA-DR (human leukocyte antigen DR) on monocytes, MerTk (Myeloid-epithelial-reproductive tyrosine kinase) on neutrophils and plasma metaloproteinase-8 (MMP8) with an area under the curve (AUC) = 0.94 [95% confidence interval (IC95): 0.91;0.97]. Among patients in whom a bacterial infection was excluded, the combination of CD64 expression, and CD24 on neutrophils and CX3CR1 on monocytes ended to an AUC = 0.98 [0.96;1] to define those with a viral infection. Conclusion: In a convenient cohort of patients admitted with a suspicion of infection, two different combinations of plasma and cell surface biomarkers were performant to identify bacterial and viral infection.

Reduced serum levels of pro-inflammatory chemokines in fragile X syndrome

BackgroundFragile X syndrome (FXS) is the most frequent cause of inherited intellectual disability and the most commonly identified monogenic cause of autism. Recent studies have shown that long-term pathological consequences of FXS are not solely confined to the central nervous system (CNS) but rather extend to other physiological dysfunctions in peripheral organs. To gain insights into possible immune dysfunctions in FXS, we profiled a large panel of immune-related biomarkers in the serum of FXS patients and healthy controls.MethodsWe have used a sensitive and robust Electro Chemi Luminescence (ECL)-based immunoassay to measure the levels of 52 cytokines in the serum of n = 25 FXS patients and n = 29 healthy controls. We then used univariate statistics and multivariate analysis, as well as an advanced unsupervised clustering method, to identify combinations of immune-related biomarkers that could discriminate FXS patients from healthy individuals.ResultsWhile the majority of the tested cytokines were present at similar levels in FXS patients and healthy individuals, nine chemokines, CCL2, CCL3, CCL4, CCL11, CCL13, CCL17, CCL22, CCL26 and CXCL10, were present at much lower levels in FXS patients. Using robust regression, we show that six of these biomarkers (CCL2, CCL3, CCL11, CCL22, CCL26 and CXCL10) were negatively associated with FXS diagnosis. Finally, applying the K-sparse unsupervised clustering method to the biomarker dataset allowed for the identification of two subsets of individuals, which essentially matched the FXS and healthy control categories.ConclusionsOur data show that FXS patients exhibit reduced serum levels of several chemokines and may therefore exhibit impaired immune responses. The present study also highlights the power of unsupervised clustering methods to identify combinations of biomarkers for diagnosis and prognosis in medicine.

Development of sepsis in relation to serum biomarkers concentration like intercellular adhesion molecule-1, macrophage migration inhibitory factor, plasminogen activator inhibitor-1 and soluble Fas ligand

Sepsis is a condition with high mortality rates and its diagnosis remains a challenge. Therefore, a new diagnostic biomarker or combinations of biomarkers for diagnosis of sepsis should be developed. In total, 80 patients who were suspected of having sepsis were followed up for 7days. Quick Sepsis-related Organ Failure Assessment (qSOFA) scores were calculated day 1 and day 7; four biomarkers (intercellular adhesion molecule-1, macrophage migration inhibitory factor, plasminogen activator inhibitor-1 and soluble Fas ligand) results were evaluated the same. In 7 days in sepsis patients, blood serum concentrations of biomarker intercellular adhesion molecule-1 (ICAM-1) increased, p=0.022.

Bayesian modeling and inference for diagnostic accuracy and probability of disease based on multiple diagnostic biomarkers with and without a perfect reference standard.

The area under the receiver operating characteristic (ROC) curve (AUC) is used as a performance metric for quantitative tests. Although multiple biomarkers may be available for diagnostic or screening purposes, diagnostic accuracy is often assessed individually rather than in combination. In this paper, we consider the interesting problem of combining multiple biomarkers for use in a single diagnostic criterion with the goal of improving the diagnostic accuracy above that of an individual biomarker. The diagnostic criterion created from multiple biomarkers is based on the predictive probability of disease, conditional on given multiple biomarker outcomes. If the computed predictive probability exceeds a specified cutoff, the corresponding subject is allocated as 'diseased'. This defines a standard diagnostic criterion that has its own ROC curve, namely, the combined ROC (cROC). The AUC metric for cROC, namely, the combined AUC (cAUC), is used to compare the predictive criterion based on multiple biomarkers to one based on fewer biomarkers. A multivariate random-effects model is proposed for modeling multiple normally distributed dependent scores. Bayesian methods for estimating ROC curves and corresponding (marginal) AUCs are developed when a perfect reference standard is not available. In addition, cAUCs are computed to compare the accuracy of different combinations of biomarkers for diagnosis. The methods are evaluated using simulations and are applied to data for Johne's disease (paratuberculosis) in cattle.

Combination of biomarkers for diagnosis of acute kidney injury after cardiopulmonary bypass

Novel acute kidney injury (AKI) biomarkers offer promise of earlier diagnosis and risk stratification, but have yet to find widespread clinical application. We measured urinary α and π glutathione S-transferases (α-GST and π-GST), urinary l-type fatty acid-binding protein (l-FABP), urinary neutrophil gelatinase-associated lipocalin (NGAL), urinary hepcidin and serum cystatin c (CysC) before surgery, post-operatively and at 24 h after surgery in 93 high risk patient undergoing cardiopulmonary bypass (CPB) and assessed the ability of these biomarkers alone and in combination to predict RIFLE-R defined AKI in the first 5 post-operative days. Twenty-five patients developed AKI. π-GST (ROCAUC = 0.75), lower urine Hepcidin:Creatine ratio at 24 h (0.77), greater urine NGAL:Cr ratio post-op (0.73) and greater serum CysC at 24 h (0.72) best predicted AKI. Linear combinations with significant improvement in AUC were: Hepcidin:Cr 24 h + post-operative π-GST (AUC = 0.86, p = 0.01), Hepcidin:Cr 24 h + NGAL:Cr post-op (0.84, p = 0.03) and CysC 24 h + post-operative π-GST (0.83, p = 0.03), notably these significant biomarkers combinations all involved a tubular injury and a glomerular filtration biomarker. Despite statistical significance in receiver–operator characteristic (ROC) analysis, when assessed by ability to define patients to two groups at high and low risk of AKI, combinations failed to significantly improve classification of risk compared to the best single biomarkers. In an alternative approach using Classification and Regression Tree (CART) analysis a model involving NGAL:Cr measurement post-op followed by Hepcidin:Cr at 24 h was developed which identified high, intermediate and low risk groups for AKI. Regression tree analysis has the potential produce models with greater clinical utility than single combined scores.