Multiple Biomarker Panels Research Articles

Plasma biomarkers of amyloid, tau, astrogliosis, and axonal injury in a mixed memory clinic cohort.

Studies have shown that blood biomarkers can differentiate dementia disorders. However, the diagnosis of dementia still relies primarily on cerebrospinal fluid and imaging modalities. The new disease-modifying treatments call for more widely applicable biomarkers. Plasma samples (n=250) from two mixed memory clinic were included. Participants were divided into amyloid beta positives (Aβ+) and Aβ negatives (Aβ-). Plasma phosphorylated tau (p-tau) 181, p-tau231, Aβ1-42 (Aβ42), Aβ40, Aβ42/Aβ40, glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL) were measured by single molecule array. Significant differences were found among cognitively unimpaired, mild cognitive impairment, Alzheimer's disease (AD), and non-AD, and nearly all of the biomarkers were able to predict amyloid status. When combining p-tau181 and p-tau231 they predicted Aβ positivity with an area under the curve (AUC) of 0.75, and when combining all biomarkers an AUC of 0.86 was found. This study supports previous findings on plasma biomarkers, even when investigated in a typical clinical setting in a consecutive, heterogeneous, mixed memory clinic. This study investigated seven plasma biomarkers in a mixed memory clinic, regardless of amyloid co-pathology or atypical phenotypes.These findings support previous promising results on plasma biomarkers, even when investigated in a heterogeneous population.The combination of phosphorylated tau (p-tau)181 and p-231 performed only slightly worse than a panel of multiple biomarkers, aligning with previous studies.Plasma biomarkers show potential for future applications in primary care, treatment monitoring, and trial selection.

Multiple Biomarkers to Predict Major Adverse Cardiovascular Events in Patients With Coronary Chronic Total Occlusions.

There are limited tools available to predict the long-term prognosis of persons with coronary chronic total occlusions (CTO). A previously-described blood biomarker panel to predict cardiovascular (CV) events was evaluated in patients with CTO. From 1251 patients in the CASABLANCA study, 241 participants with a CTO were followed for an average of 4 years for occurrence of major adverse CV events (MACE, CV death, non-fatal myocardial infarction or stroke) and CV death/heart failure (HF) hospitalization. Results of a biomarker panel (kidney injury molecule-1, N-terminal pro-B-type natriuretic peptide, osteopontin, and tissue inhibitor of metalloproteinase-1) from baseline samples were expressed as low-, medium-, and high-risk. By 4 years, a total of 67 (27.8%) MACE and 56 (23.2%) CV death/HF hospitalization events occurred. The C-statistic of the panel for MACE through 4 years was 0.79 (P <0.001). Considering the low-risk group as referent, the hazard ratio (HR) of MACE by 4 years was 6.65 (95% confidence interval [CI]: 2.98-14.8) and 12.4 (95% CI:5.17-29.6) for the medium and high-risk groups (both P <0.001). The C-statistic for CVD/HF hospitalization by 4 years was 0.84 (P <0.001). Compared to the low-risk score group, the medium and high-risk groups had HR of 5.61 (95% CI: 2.33-13.5) and 15.6 (95% CI: 6.18, 39.2; both P value <0.001). In conclusion, a multiple biomarker panel assisted in discriminating a broad range of risk for adverse outcomes in patients with coronary CTO. These results may have implications for risk stratification, patient care and could have a role for clinical trial enrichment.

Diagnostic Potential of Cytokine Biomarkers in Endometriosis: Challenges and Insights.

Endometriosis is a common gynecological condition affecting approximately 10% of women of reproductive age, characterized by the abnormal presence of endometrial-like tissue outside the uterus. Although endometriosis was first described over 300 years ago, its underlying mechanisms remain poorly understood, and accurate, prompt diagnosis continues to be challenging. Currently, there is a lack of effective, non-invasive diagnostic methods, and available treatments often come with significant side effects and high recurrence rates. This has spurred interest in investigating the role of pro- and anti-inflammatory molecules, particularly cytokines, in endometriosis, as these molecules play a key role in its progression by influencing cell growth and differentiation. Previous studies suggest that various cytokines could serve as potential biomarkers for diagnosing endometriosis, as they are detectable in both serum and peritoneal fluid. This review provides an overview of the expression, origin, function, and regulation of specific cytokines in endometriosis, along with a brief discussion on their potential clinical implications for diagnosis. Due to the complexity of endometriosis, a panel of multiple biomarkers may ultimately be necessary for accurate diagnosis. It is essential to consider factors such as patient selection, sample collection, and analytical variability when initiating or evaluating biomarker studies.

Plasma metabolomics and lipidomics reveal potential novel biomarkers in early gastric cancer: An explorative study.

Early identification and therapy can significantly improve the outcome for gastric cancer. However, there is still no perfect biomarker available for the detection of early gastric cancer. This study aimed to investigate the alterations in the plasma metabolites of early gastric cancer using metabolomics and lipidomics based on high-performance liquid chromatography-mass spectrometry (HPLC-MS), which detected potential biomarkers that could be used for clinical diagnosis. To investigate the changes in metabolomics and lipidomics, a total of 30 plasma samples were collected, consisting of 15 patients with early gastric cancer and 15 healthy controls. Extensive HPLC-MS-based untargeted metabolomic and lipidomic investigations were conducted. Differential metabolites and metabolic pathways were uncovered through the utilization of statistical analysis and bioinformatics analysis. Candidate biomarker screening was performed using support vector machine-based multivariate receiver operating characteristic analysis. A disturbance was observed in a combined total of 19 metabolites and 67 lipids of the early gastric cancer patients. The analysis of KEGG pathways showed that the early gastric cancer patients experienced disruptions in the arginine biosynthesis pathway, the pathway for alanine, aspartate, and glutamate metabolism, as well as the pathway for glyoxylate and dicarboxylate metabolism. Plasma metabolomics and lipidomics have identified multiple biomarker panels that can effectively differentiate early gastric cancer patients from healthy controls, exhibiting an area under the curve exceeding 0.9. These metabolites and lipids could potentially serve as biomarkers for the screening of early gastric cancer, thereby optimizing the strategy for the detection of early gastric cancer. The disrupted pathways implicated in early gastric cancer provide new clues for additional understanding of gastric cancer's pathogenesis. Nonetheless, large-scale clinical data are required to prove our findings.

Potential Utility of Plasma Biomarker Panels in Differential Diagnosis of Alzheimer's Disease.

Blood tests are in need, in the clinical diagnosis of Alzheimer's disease (AD) as minimally invasive and less expensive alternatives to cerebrospinal fluid and neuroimaging methods. On these lines, single molecule array (Simoa) analysis of amyloid-β (Aβ42), total tau (t-tau), phospho-tau (p-tau 181), and neurofilament L (NfL) in the plasma samples of AD subjects, healthy controls (HC), and non-AD subjects was conducted. Findings from this study suggest that a panel of multiple plasma biomarkers (NfL, Aβ42, t-tau, and p-tau 181) combined with the clinical assessments could support differential diagnosis of AD and other dementias from healthy controls.

Identification and verification of plasma protein biomarkers that accurately identify an ectopic pregnancy

BackgroundDifferentiating between a normal intrauterine pregnancy (IUP) and abnormal conditions including early pregnancy loss (EPL) or ectopic pregnancy (EP) is a major clinical challenge in early pregnancy. Currently, serial β-human chorionic gonadotropin (β-hCG) and progesterone are the most commonly used plasma biomarkers for evaluating pregnancy prognosis when ultrasound is inconclusive. However, neither biomarker can predict an EP with sufficient and reproducible accuracy. Hence, identification of new plasma biomarkers that can accurately diagnose EP would have great clinical value.MethodsPlasma was collected from a discovery cohort of 48 consenting women having an IUP, EPL, or EP. Samples were analyzed by liquid chromatography-tandem mass spectrometry (LC-MS/MS) followed by a label-free proteomics analysis to identify significant changes between pregnancy outcomes. A panel of 14 candidate biomarkers were then verified in an independent cohort of 74 women using absolute quantitation by targeted parallel reaction monitoring mass spectrometry (PRM-MS) which provided the capacity to distinguish between closely related protein isoforms. Logistic regression and Lasso feature selection were used to evaluate the performance of individual biomarkers and panels of multiple biomarkers to predict EP.ResultsA total of 1391 proteins were identified in an unbiased plasma proteome discovery. A number of significant changes (FDR ≤ 5%) were identified when comparing EP vs. non-EP (IUP + EPL). Next, 14 candidate biomarkers (ADAM12, CGA, CGB, ISM2, NOTUM, PAEP, PAPPA, PSG1, PSG2, PSG3, PSG9, PSG11, PSG6/9, and PSG8/1) were verified as being significantly different between EP and non-EP in an independent cohort (FDR ≤ 5%). Using logistic regression models, a risk score for EP was calculated for each subject, and four multiple biomarker logistic models were identified that performed similarly and had higher AUCs than models with single predictors.ConclusionsOverall, four multivariable logistic models were identified that had significantly better prediction of having EP than those logistic models with single biomarkers. Model 4 (NOTUM, PAEP, PAPPA, ADAM12) had the highest AUC (0.987) and accuracy (96%). However, because the models are statistically similar, all markers in the four models and other highly correlated markers should be considered in further validation studies.

Prediction of maternal and foetal outcomes among patients with preeclampsia using circulatory biomarkers (MMP-9 & ST2) - A prospective cohort study

IntroductionPreeclampsia is one of the major causes of maternal and foetal morbidity and mortality worldwide. Its complications include but are not limited to eclampsia, intracerebral haemorrhage and cardiovascular diseases in the later stages of life. The combination of clinical and risk variables and a panel of multiple biomarkers will help clinicians in risk stratification and prognostication of clinical outcomes among preeclamptic women. We evaluated MMP-9 (matrix metalloproteinase - 9) and ST2 (suppression of tumorigenicity 2) for utility as biomarkers and for predicting maternal and foetal outcomes in women with preeclampsia. MethodsThis prospective cohort study involved 49 preeclamptic women and 80 healthy controls. Biomarkers were measured in plasma using ELISA. The patients were followed up to assess maternal and foetal outcomes. ResultsThe mean value of MMP-9 was 2.42 ng/mL in the preeclamptic group and 2.67 ng/mL in controls. The mean value of ST2 (1937.4 ± 747.81) in the preeclamptic group was high compared to the control group (1005.7 ± 683.6) and the difference was significant (P = 0.0001). The study population was divided into those with high and low MMP-9 and those with high and low ST2. Lower levels of MMP-9 seemed to be related to both early and late onset preeclampsia. The ROC (Receiver Operating Characteristic) curve did not show the ability to predict maternal and foetal outcomes. DiscussionOur study demonstrated that women with preeclampsia had low MMP-9 and high ST2 compared to healthy pregnant women. But neither of the biomarkers could predict complications of preeclampsia.

Development of multiple biomarker panels for prediction of sarcopenia in community-dwelling older adults

BackgroundIt is required to consider multiple biomarkers simultaneously to predict sarcopenia and to understand its complex pathological mechanisms. This study aimed to develop multiple biomarker panels for predicting sarcopenia in older adults and to further examine its association with the incidence of sarcopenia. MethodsA total of 1,021 older adults were selected from the Korean Frailty and Aging Cohort Study. Sarcopenia was defined by the Asian Working Group for Sarcopenia 2019 criteria. Among the 14 biomarker candidates at baseline, eight biomarkers that could optimally detect individuals with sarcopenia were selected to develop a multi-biomarker risk score (range from 0 to 10). The utility of developed multi-biomarker risk score in discriminating sarcopenia was investigated using receiver operating characteristic (ROC) analysis. ResultsThe multi-biomarker risk score had an area under the ROC curve (AUC) of 0.71 with an optimal cut-off of 1.76 score, which was significantly higher than all single biomarkers with AUC of <0.7 (all, p<0.01). During the two-year follow-up, the incidence of sarcopenia was 11.1%. Continuous multi-biomarker risk score was positively associated with incidence of sarcopenia after adjusting confounders (odds ratio [OR]=1.63; 95% confidence interval [CI]=1.23–2.17). Participants with a high risk score had higher odds of sarcopenia than those with a low risk score (OR=1.82; 95% CI=1.04–3.19). ConclusionsMulti-biomarker risk score, which was a combination of eight biomarkers with different pathophysiologies, better discriminated the presence of sarcopenia than a single biomarker, and it could further predict the incidence of sarcopenia over two years in older adults.

High-throughput proteomics of breast cancer subtypes: Biological characterization and multiple candidate biomarker panels to patients' stratification

Background and aimsThe actual classification of breast tumors in subtypes represents an attempt to stratify patients into clinically cohesive groups, nevertheless, clinicians still lack reproducible and reliable protein biomarkers for breast cancer subtype discrimination. In this study, we aimed to access the differentially expressed proteins between these tumors and its biological implications, contributing to the subtype's biological and clinical characterization, and with protein panels for subtype discrimination. MethodsIn our study, we applied high-throughput mass spectrometry, bioinformatic, and machine learning approaches to investigate the proteome of different breast cancer subtypes. ResultsWe identified that each subtype depends on different protein expression patterns to sustain its malignancy, and also alterations in pathways and processes that can be associated with each subtype and its biological and clinical behaviors. Regarding subtype biomarkers, our panels achieved performances with at least 75% of sensibility and 92% of specificity. In the validation cohort, the panels obtained acceptable to outstanding performances (AUC = 0.740 to 1.00). ConclusionsIn general, our results expand the accuracy of breast cancer subtypes' proteomic landscape and improve the understanding of its biological heterogeneity. In addition, we identified potential protein biomarkers for the stratification of breast cancer patients, improving the repertoire of reliable protein biomarkers. SignificanceBreast cancer is the most diagnosed cancer type worldwide and the most lethal cancer in women. As a heterogeneous disease, breast cancer tumors can be classified into four major subtypes, each presenting particular molecular alterations, clinical behaviors, and treatment responses. Thus, a pivotal step in patient management and clinical decisions is accurately classifying breast tumor subtypes. Currently, this classification is made by the immunohistochemical detection of four classical markers (estrogen receptor, progesterone receptor, HER2 receptor, and the Ki-67 index); however, it is known that these markers alone do not fully discriminate the breast tumor subtypes. Also, the poor understanding of the molecular alterations of each subtype leads to a challenging decision-making process regarding treatment choice and prognostic determination. This study, through high-throughput label-free mass-spectrometry data acquisition and downstream bioinformatic analysis, advances in the proteomic discrimination of breast tumors and achieves an in-depth characterization of the subtype's proteomes. Here, we indicate how the variations in the subtype's proteome can influence the tumor's biological and clinical differences, highlighting the variation in the expression pattern of oncoproteins and tumor suppressor proteins between subtypes. Also, through our machine-learning approach, we propose multi-protein panels with the potential to discriminate the breast cancer subtypes. Our panels achieved high classification performance in our cohort and in the independent validation cohort, demonstrating their potential to improve the current tumor discrimination system as complements to the classical immunohistochemical classification.

Evaluation of Serum and Urine Biomarker Panels for Developmental Dysplasia of the Hip Prior to Onset of Secondary Osteoarthritis.

Evaluate serum and urine biomarker panels for their capabilities in discriminating between individuals (13- to 34-years-olds) with healthy hips versus those with developmental dysplasia of the hip (DDH) prior to diagnosis of secondary hip osteoarthritis (OA). Urine and serum were collected from individuals (15-33 years old) with DDH, prior to and following diagnosis of hip OA, and from age-matched healthy-hip controls. Samples were analyzed for panels of protein biomarkers with potential for differentiation of hip status using receiver operator characteristic curve (area under curve [AUC]) assessments. Multiple urine and serum biomarker panels effectively differentiated individuals with DDH from healthy-hip controls in a population at risk for developing secondary hip OA with the best performing panel demonstrating an AUC of 0.959. The panel comprised of two serum and two urinary biomarkers provided the highest combined values for sensitivity, 0.85, and specificity, 1.00, while a panel of four serum biomarkers provided the highest sensitivity, 0.93, while maintaining adequate specificity, 0.71. Results of this study indicate that panels of protein biomarkers measured in urine and serum may be able to differentiate young adults with DDH from young adults with healthy hips. These data suggest the potential for clinical application of a routine diagnostic method for cost-effective and timely screening for DDH in at-risk populations. Further development and validation of these biomarker panels may result in highly sensitive and specific tools for early diagnosis, staging, and prognostication of DDH, as well as treatment decision making and monitoring capabilities. III.

Development of Advanced Imaging and Molecular Imaging for Barrett's Neoplasia.

Barrett esophagus (BE) is a precursor to a life-threatening esophageal adenocarcinoma (EAC). Surveillance endoscopy with random biopsies is recommended for early intervention against EAC, but its adherence in the clinical setting is poor. Dysplastic lesions with flat architecture and patchy distribution in BE are hardly detected by high-resolution endoscopy, and the surveillance protocol entails issues of time and labor and suboptimal interobserver agreement for diagnosing dysplasia. Therefore, the development of advanced imaging technologies is necessary for Barrett’s surveillance. Recently, non-endoscopic or endoscopic technologies, such as cytosponge, endocytoscopy, confocal laser endomicroscopy, autofluorescence imaging, and optical coherence tomography/volumetric laser endomicroscopy, were developed, but most of them are not clinically available due to the limited view field, expense of the equipment, and significant time for the learning curve. Another strategy is focused on the development of molecular biomarkers, which are also not ready to use. However, a combination of advanced imaging techniques together with specific biomarkers is expected to identify morphological abnormalities and biological disorders at an early stage in the surveillance. Here, we review recent developments in advanced imaging and molecular imaging for Barrett’s neoplasia. Further developments in multiple biomarker panels specific for Barrett’s HGD/EAC include wide-field imaging systems for targeting ‘red flags’, a high-resolution imaging system for optical biopsy, and a computer-aided diagnosis system with artificial intelligence, all of which enable a real-time and accurate diagnosis of dysplastic BE in Barrett’s surveillance and provide information for precision medicine.

Prediction of Liver Triglyceride Content in Early Lactation Multiparous Holstein Cows Using Blood Metabolite, Mineral, and Protein Biomarker Concentrations.

Simple SummaryBovine fatty liver syndrome is a metabolic disorder in transition dairy cows that has been associated with adverse consequences such as lower milk production and fertility. Fatty liver syndrome is difficult to monitor and diagnose in applied practice and research settings because it requires a liver tissue biopsy to determine liver triglyceride content. This study aimed to develop and validate a panel of blood metabolite, protein, and mineral biomarkers as a less invasive and more accessible tool to assess liver triglyceride content. We investigated a variety of panels using blood measurements from a single timepoint or multiple timepoints, as well as different combinations of biomarkers based on their perceived accessibility. Both the single and multiple timepoint biomarker panels accurately classified cows with high liver triglyceride content (top 33.3% vs. lower 66.7%), but accuracy was lower for classifying cows with or without maximum liver triglyceride in the top 50% or top 66.7% of liver triglyceride content. We suggest that the blood biomarker models predicting high triglyceride content may be useful for monitoring fatty liver in research and applied practice, as well as enable larger scale research studies investigating fatty liver in dairy cows.Bovine fatty liver syndrome (bFLS) is difficult to diagnose because a liver tissue biopsy is required to assess liver triglyceride (TG) content. We hypothesized that a blood biomarker panel could be a convenient alternative method of liver TG content assessment and bFLS diagnosis. Our objectives were to predict liver TG using blood biomarker concentrations across days in milk (DIM; longitudinal, LT) or at a single timepoint (ST; 3, 7, or 14 DIM), as well as different biomarker combination based on their perceived accessibility. Data from two separate experiments (n = 65 cows) was used for model training and validation. Response variables were based on the maximum liver TG observed in 1 and 14 DIM liver biopsies: Max TG (continuous), Low TG (TG > 13.3% dry matter; DM), Median TG (TG > 17.1% DM), and High TG (TG > 22.0% DM). Model performance varied but High TG was well predicted by sparse partial least squares—discriminate analysis models using LT and ST data, achieving balanced error rates ≤ 15.4% for several model variations during cross-validation. In conclusion, blood biomarker panels using 7 DIM, 14 DIM, or LT data may be a useful diagnostic tool for bFLS in research and field settings.

Review of potential biomarkers of inflammation and kidney injury in diabetic kidney disease.

Diabetic kidney disease is expected to increase rapidly over the coming decades with rising prevalence of diabetes worldwide. Current measures of kidney function based on albuminuria and estimated glomerular filtration rate do not accurately stratify and predict individuals at risk of declining kidney function in diabetes. As a result, recent attention has turned towards identifying and assessing the utility of biomarkers in diabetic kidney disease. This review explores the current literature on biomarkers of inflammation and kidney injury focussing on studies of single or multiple biomarkers between January 2014 and February 2020. Multiple serum and urine biomarkers of inflammation and kidney injury have demonstrated significant association with the development and progression of diabetic kidney disease. Of the inflammatory biomarkers, tumour necrosis factor receptor‐1 and ‐2 were frequently studied and appear to hold most promise as markers of diabetic kidney disease. With regards to kidney injury biomarkers, studies have largely targeted markers of tubular injury of which kidney injury molecule‐1, beta‐2‐microglobulin and neutrophil gelatinase‐associated lipocalin emerged as potential candidates. Finally, the use of a small panel of selective biomarkers appears to perform just as well as a panel of multiple biomarkers for predicting kidney function decline.

Multiple serum biomarkers for predicting suicidal behaviours in depressive patients receiving pharmacotherapy.

Predictive values of multiple serum biomarkers for suicidal behaviours (SBs) have rarely been tested. This study sought to evaluate and develop a panel of multiple serum biomarkers for predicting SBs in outpatients receiving a 12-month pharmacotherapy programme for depressive disorders. At baseline, 14 serum biomarkers and socio-demographic/clinical characteristics including previous suicidal attempt and present suicidal severity were evaluated in 1094 patients with depressive disorders without a bipolar diagnosis. Of these, 884 were followed for increased suicidal severity and fatal/non-fatal suicide attempt outcomes over a 12-month treatment period. Individual and combined effects of serum biomarkers on these two prospective SBs were estimated using logistic regression analysis after adjustment for relevant covariates. Increased suicidal severity and fatal/non-fatal suicide attempt during the 12-month pharmacotherapy were present in 155 (17.5%) and 38 (4.3%) participants, respectively. Combined cortisol, total cholesterol, and folate serum biomarkers predicted fatal/non-fatal suicide attempt, and these with interleukin-1 beta and homocysteine additionally predicted increased suicidal severity, with clear gradients robust to adjustment (p values < 0.001). Application of multiple serum biomarkers could considerably improve the predictability of SBs during the outpatient treatment of depressive disorders, potentially highlighting the need for more frequent monitoring and risk appraisal.

Multiple biomarkers are more accurate than a combination of carbohydrate antigen 125 and human epididymis protein 4 for ovarian cancer screening.

ObjectiveThe objective of this study was to compare and evaluate the diagnostic value of serum carbohydrate antigen 125 (CA125) and/or human epididymis protein 4 (HE4) and a panel of novel multiple biomarkers in patients with ovarian tumors to identify more accurate and effective markers for screening ovarian cancer.MethodsCandidate ovarian cancer biomarkers were selected based on a literature search. Dozens of candidate biomarkers were examined using 143 serum samples from patients with ovarian cancer and 157 healthy serum samples as non-cancer controls. To select the optimal marker panel for an ovarian cancer classification model, a set of biomarker panels was created with the number of possible combinations of eight biomarkers. Using the set of biomarkers as an input variable, the optimal biomarker panel was selected by examining the performance of the biomarker panel set using the Random Forest algorithm as a non-linear classification method and a 10-fold cross-validation technique.ResultsThe final selected optimal combination of five biomarkers (CA125, HE4, cancer antigen 15-3, apolipoprotein [Apo] A1, and ApoA2) exhibited a sensitivity of 93.71% and specificity of 93.63% for ovarian cancer detection during validation.ConclusionCombining multiple biomarkers is a valid strategy for ovarian cancer diagnosis and can be used as a minimally invasive screening method for early ovarian cancer. A panel of five optimal biomarkers, including CA125 and HE4, was verified in this study. These can potentially be used as clinical biomarkers for early detection of ovarian cancer.

Abstract 10637: A Pooled Multi-National Validation Study of a Machine Learning, High-Sensitivity Troponin-Based Multi-Proteomic Model to Predict the Presence of Obstructive Coronary Artery Disease as Compared to High Sensitivity Troponin Alone in a Subset of Patients with Diabetes

Introduction: Diabetes Mellitus (DM) is a major risk factor for coronary artery disease (CAD) associated with a two-fold increase in mortality. We aimed to validate the performance of a machine learning based multi-biomarker diagnostic panel to predict obstructive coronary artery disease (oCAD) compared to high-sensitivity cardiac troponin-I (hs-cTnI) alone in a subset of patients with DM. Methods: A previously developed multiple biomarker scoring model was utilized. The pooled cohort included 132 patients at Inova Fairfax Hospital, 69 patients at Massachusetts General Hospital and 46 patients at the University Hospital Hamburg-Eppendorf with a mixture of acute and lesser acute presentations. Three clinical factors (sex, age, and previous coronary percutaneous intervention) and three biomarkers (hs-cTnI, Adiponectin, and Kidney Injury Molecule-1) were combined. oCAD was defined as &gt;50% coronary obstruction in at least one coronary artery (for the University Hospital Hamburg-Eppendorf cohort) or &gt;70% coronary obstruction in at least one coronary artery (for the other two cohorts). The multiple biomarker diagnostic panel’s performance to predict oCAD was also compared to hs-cTnI alone. Results: The multiple protein panel had an area under the receiver-operating characteristic curve of 0.77 (95% CI, 0.70, 0.84, p &lt;0.001) for the presence of oCAD (Figure 1). At optimal cutoff, the score had 79% sensitivity, 57% specificity, and a positive predictive value of 81% for oCAD. The multiple biomarker panel had a diagnostic odds ratio of 4.82 (95% CI 2.68, 8.67, p&lt;0.01). In comparison, in patients without an acute MI, hs-cTnI alone had an area under the receiver-operating characteristic curve of 0.53 (95% CI, 0.49, 0.57, p = 0.253) for oCAD (Figure 1). Conclusions: In this multinational pooled cohort, a previously described novel machine learning, multiple protein biomarker panel provided high accuracy to diagnose patients for oCAD in a subset of patients with DM.

A pooled multi-national validation study of a machine learning, high-sensitivity troponin-based multi-proteomic model to predict the presence of obstructive coronary artery disease

Abstract Background Undetected obstructive coronary artery disease (oCAD) is a global health problem associated with significant morbidity and mortality. A need exists for an accurate and easily accessible diagnostic test for oCAD. Using machine learning, a multi-biomarker blood diagnostic test for oCAD based on high-sensitivity cardiac troponin-I (hs-cTnI) has been developed. Purpose To validate the performance of a previously developed, algorithmically weighted, multiple protein diagnostic panel to diagnose oCAD in a pooled multi-national cohort and to compare the diagnostic panel's performance to predict oCAD to hs-cTnI alone. Methods Three clinical factors (sex, age, and previous coronary percutaneous intervention) and three biomarkers (hs-cTnI, Adiponectin, and Kidney Injury Molecule-1) were combined. hs-cTnI blood samples were assayed on the Siemens Atellica and Abbott Diagnostics ARCHITECT immunoassay platforms. Adiponectin and Kidney Injury Molecule-1 were measured with a multiplex assay on blood samples via the Luminex 100/200 xMAP platform. Individual data from a total of 924 patients with a mixture of acute and lesser acute presentations from three centers were pooled (Table 1). oCAD was defined as &amp;gt;50% coronary obstruction in at least one coronary artery (for the University Hospital Hamburg-Eppendorf cohort) or &amp;gt;70% coronary obstruction in at least one coronary artery (for the other two cohorts). The multiple biomarker diagnostic panel's performance to predict oCAD was also compared to hs-cTnI alone. Results The multiple protein panel had an area under the receiver-operating characteristic curve of 0.80 (95% CI, 0.77, 0.83, p&amp;lt;0.001) for the presence of oCAD (Figure 1). At optimal cutoff, the score had 74% sensitivity, 72% specificity, and a positive predictive value of 81% for oCAD. The multiple biomarker panel had a diagnostic odds ratio of 7.48 (95% CI 5.55, 10.09, p&amp;lt;0.001). In comparison, in patients without an acute MI, hs-cTnI alone had an area under the receiver-operating characteristic curve of 0.63 (95% CI, 0.60, 0.67, p&amp;lt;0.001)) for oCAD (Figure 1). Conclusions In this multinational pooled cohort, a previously described novel machine learning, multiple biomarker panel provided high accuracy to diagnose patients for oCAD. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): Prevencio, Inc. Table 1. Pooled Variable DataFigure 1. ROC for HART CADhs and hs-cTnI

Biomarkers for respiratory diseases: Present applications and future discoveries

AbstractBiomarkers such as clinical characteristics and laboratory indices have been widely used in many respiratory diseases, such as chronic airway diseases, lung infection, lung cancer and acute respiratory distress syndrome. As such, various non‐invasive and invasive samples, such as blood, urine, induced sputum, bronchoalveolar lavage fluid and lung biopsy are currently being collected. Omics‐based discovery strategies facilitate the identification of next‐generation candidate biomarkers. The identification and integration of multiple omics biomarkers (genomics, epigenomics, proteomics, metabolomics and radiomics) can answer many unsolved questions about respiratory diseases. The ultimate realization of the translation of novel biomarkers into clinical practice is a major challenge because good biomarkers must be sensitive, technically feasible, non‐invasive, non‐expensive and most importantly, validated. The four stages of clinical trial design in the development of biomarkers are also discussed. The concept of systems biology not only focuses on novel molecular biomarkers but also integrates clinical, laboratory and omics information as a whole. Finally, developing multiple biomarker panels is a goal to improve the healthcare of respiratory diseases.

Multiple biomarker panel for the detection of circulating tumor cells in peripheral blood of breast cancer patients for early detection using the real-time reverse transcription-polymerase chain reaction

Multiple biomarker panel for the detection of circulating tumor cells in peripheral blood of breast cancer patients for early detection using the real-time reverse transcription-polymerase chain reaction

MO466A NOVEL PREDICTION MARKER OF 10-YEAR RISK AND LIFETIME RISK OF RECURRENT CARDIOVASCULAR EVENTS IN PATIENTS WITH CKD AND DIABETES MELLITUS

Abstract Background and Aims Chronic kidney disease (CKD) is one of the most prevalent complication of Diabetes Mellitus and patients with both diseases are more exposed to atherosclerosis injury and premature death from cardiovascular disease (CVD). CVD is worsened by inflammation, oxidative stress, lipid accumulation and high-density lipoprotein cholesterol (HDL) reduction: patients with altered lipid metabolism more often present monocyte profile changes, with an altered pro-inflammatory phenotype leading to a significant risk of plaque formation and atherosclerosis. The alteration of the balance between monocyte and HDL, represented by the Monocyte/HDL ratio (MHR), is an easy way to evaluate the inflammatory status and to study appropriate strategies to treat high-risk patients. Method We evaluated 150 consecutive diabetic patients with CKD hospitalized in the Unit of Nephrology and Dialysis of the Policlinic G. Martino of Messina, Italy, with a history of CVD. We used the SMART-REACH SCORE, a model created to estimate life expectancy without recurrent cardiovascular events for individuals with a history of CVD. We performed a retrospective analysis of the MHR status of our patients to study its correlations with the 10-year risk and lifetime risk for myocardial infarction, stroke or vascular death, recurrent CVD events and free life-expectancy if standard care is provided. Results In the entire cohort of patients, MHR appeared to be positively correlated with ten-year risk (ρ=0.469; P &amp;lt;0.0001) (Figure 1) and lifetime risk of myocardial infarction, stroke, or CV death (ρ=0.428; P &amp;lt;0.0001) (Figure 2). Furthermore, patients with higher MHR levels had a significantly smaller number of years of CVD-free life expectancy (ρ=-0.364; P &amp;lt;0.0001) (Figure 3). Conclusion MHR can represent a valid tool to predict the recurrence of CVD in CKD patients with diabetes. This easy-to-perform marker of oxidative stress and CVD risk can be used alone or in a multiple biomarker panel, improving the stratification and management of patients with comorbidities and risk of life- threatening conditions.