Analysis of biomarker data: logs, odds ratios, and receiver operating characteristic curves

From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies

In the dynamic landscape of targeted therapeutics, drug discovery has pivoted towards understanding underlying disease mechanisms, placing a strong emphasis on molecular perturbations and target identification. This paradigm shift, crucial for drug discovery, is underpinned by big data, a transformative force in the current era. Omics data, characterized by its heterogeneity and enormity, has ushered biological and biomedical research into the big data domain. Acknowledging the significance of integrating diverse omics data strata, known as multi-omics studies, researchers delve into the intricate interrelationships among various omics layers. This review navigates the expansive omics landscape, showcasing tailored assays for each molecular layer through genomes to metabolomes. The sheer volume of data generated necessitates sophisticated informatics techniques, with machine-learning (ML) algorithms emerging as robust tools. These datasets not only refine disease classification but also enhance diagnostics and foster the development of targeted therapeutic strategies. Through the integration of high-throughput data, the review focuses on targeting and modeling multiple disease-regulated networks, validating interactions with multiple targets, and enhancing therapeutic potential using network pharmacology approaches. Ultimately, this exploration aims to illuminate the transformative impact of multi-omics in the big data era, shaping the future of biological research.

The cardio-metabolic protein profile is associated with development of type 2 diabetes at long term follow-up after gestational diabetes mellitus: Results from the OGFUS study

AIMS

Women with previous gestational diabetes mellitus (GDM) have an increased risk for later development of type 2 diabetes. During pregnancy, GDM affects the cardio-metabolic protein profile; however, it is unknown how GDM affects the cardio-metabolic protein profile in the long term and if it is associated with type 2 diabetes after GDM. We hypothesise that the cardio-metabolic protein profile is affected long term and is associated with the development of type 2 diabetes after GDM.

MATERIALS AND METHODS

A case-control follow-up study based on the Odense GDM Follow-Up Study (OGFUS) cohort, which included women with previous GDM (n = 128) and matched controls without previous GDM (n = 70). Blood samples from a follow-up assessment 8-10 years after delivery were analysed using a 29-plex panel of apolipoproteins, transport and inflammation/immune proteins using multiple-reaction-monitoring mass spectrometry.

RESULTS

Apolipoprotein A-I, D and M were significantly lower in women with previous GDM compared to controls (all p < 0.001), while apolipoprotein L-I, H, vitamin D binding protein, CRP, vitronectin, transthyretin and complement factors 3 and B were significantly higher (p = 0.008, p < 0.001, p < 0.001, p < 0.001, p < 0.001, p = 0.005, respectively). All associations remained significant after adjustment for multiple testing except CRP, whereas apolipoprotein D, vitronectin and complement factors 3 and B were associated with the development of type 2 diabetes in women with previous GDM (p = 0.02, p = 0.001, p < 0.001, p = 0.004, respectively).

CONCLUSIONS

The cardio-metabolic protein profile 8-10 years after pregnancy is altered in women with previous GDM. Apolipoprotein D, vitronectin and complement factors 3 and B are candidate risk markers of type 2 diabetes after GDM pregnancy.

α-tocopherol deficiency in follicular ovarian cyst (FOCs) follicular fluid (FF) elevates oxidative stress and impairs oocyte maturation

Follicular ovarian cysts (FOCs) are prevalent reproductive disorders in both humans and animals, especially in livestock, where they cause economic losses by reducing fertility and productivity. FOCs are marked by a dominant follicle that fails to ovulate, disrupting the estrous cycle and reproductive efficiency. Previous studies indicate that the follicular fluid (FF) in cystic ovaries shows oxidative imbalance, affecting oocyte quality by altering glutathione peroxidase (GPX1) and selenium pathways. However, the metabolic profile of FF in cystic ovaries needs further exploration. This study examined oxidative stress and metabolic changes in FOC pathogenesis. Using untargeted metabolomics of goat FF, we found significant differences in 12,741 metabolites between cystic and control FF. Cystic FF had reduced levels of α-tocopherol and 8'-apocaroten-8'-ol, key for oxidative stress management, and increased levels of mycotoxins (e.g., Deoxynivalenol-3-glucoside) and long-chain fatty acids. Adding 200 μM α-tocopherol to FOC FF oocyte cultures doubled oocyte maturation rates and decreased reactive oxygen species (ROS). Metabolomic analysis linked low α-tocopherol to high lipid peroxyl radicals and low glutathione oxidation, emphasizing oxidative stress regulation's importance in the follicular microenvironment. Our findings suggest that α-tocopherol may serve as a biomarker and therapeutic agent to enhance oocyte maturation in FOCs.

Utility of Polygenic Risk Scores (PRSs) in Predicting Pancreatic Cancer: A Systematic Review and Meta-Analysis of Common-Variant and Mixed Scores with Insights into Rare Variant Analysis

Pancreatic adenocarcinoma is the most common histological subtype of pancreatic cancer, representing approximately 85% of all cases [...].

Protein Biomarkers of Ultra-Processed Food Consumption and Risk of Coronary Heart Disease, Chronic Kidney Disease, and All-Cause Mortality

BACKGROUND

There is a need to understand the underlying biological mechanisms through which ultra-processed foods negatively affect health. Proteomics offers a valuable tool with which to examine different aspects of ultra-processed foods and their impact on health.

OBJECTIVES

The aim of this study was to identify protein biomarkers of usual ultra-processed food consumption and assess their relation to the incidence of coronary heart disease (CHD), chronic kidney disease (CKD), and all-cause mortality risk.

METHODS

A total of 9361 participants from the Atherosclerosis Risk in Communities visit 3 (1993-1995) were included. Dietary intake was assessed using a 66-item food-frequency questionnaire and the processing levels were categorized on the basis of the Nova classification. Plasma proteins were detected using an aptamer-based proteomic assay. We used multivariable linear regressions to examine the association between ultra-processed food and proteins, and Cox proportional hazard models to identify associations between ultra-processed food-related proteins and health outcomes. Models extensively controlled for sociodemographic characteristics, health behaviors, and clinical factors.

RESULTS

Eight proteins (6 positive, 2 negative) were identified as significantly associated with ultra-processed food consumption. Over a median follow-up of 22 y, there were 1276, 3084, and 5127 cases of CHD, CKD, and death, respectively. Three, 5, and 3 ultra-processed food-related proteins were associated with each outcome, respectively. One protein (β-glucuronidase) was significantly associated with a higher risk of all 3 outcomes, and 3 proteins (receptor-type tyrosine-protein phosphatase U, C-C motif chemokine 25, and twisted gastrulation protein homolog 1) were associated with a higher risk of 2 outcomes.

CONCLUSIONS

We identified a panel of protein biomarkers that were significantly associated with ultra-processed food consumption. These proteins may be considered potential biomarkers for ultra-processed food intake and may elucidate the biological processes through which ultra-processed foods impact health outcomes.

LOCC: a novel visualization and scoring of cutoffs for continuous variables with hepatocellular carcinoma prognosis as an example

BACKGROUND

The interpretation of large datasets, such as The Cancer Genome Atlas (TCGA), for scientific and research purposes, remains challenging despite their public availability. In this study, we focused on identifying gene expression profiles most relevant to patient prognosis and aimed to develop a method and database to address this issue. To achieve this, we introduced Luo's Optimization Categorization Curve (LOCC), an innovative tool for visualizing and scoring continuous variables against dichotomous outcomes. To demonstrate the efficacy of LOCC using real-world data, we analyzed gene expression profiles and patient data from TCGA hepatocellular carcinoma samples.

RESULTS

To showcase LOCC, we demonstrate an optimal cutoff for E2F1 expression in hepatocellular carcinoma, which was subsequently validated in an independent cohort. Compared to ROC curves and their AUC, LOCC offered a superior description of the predictive value of E2F1 expression across various cancer types. The LOCC score, comprised of factors representing significance, range, and impact of the biomarker, facilitated the ranking of all gene expression profiles in hepatocellular carcinoma, aiding in the evaluation and understanding of previously published prognostic gene signatures. We also demonstrate that LOCC does not have the same assumptions required of Cox proportional hazards modeling for accurate analysis. Repeated sampling demonstrated that LOCC scores outperformed ROC's AUC in discriminating predictors from non-predictors. Additionally, gene set enrichment analysis revealed significant associations between certain genes and prognosis, such as E2F target genes and G2M checkpoint with poor prognosis, and bile acid metabolism and oxidative phosphorylation with good prognosis.

CONCLUSION

In summary, we present LOCC as a novel visualization tool for the analysis of gene expression in cancer, particularly for understanding and selecting cutoffs. Our findings suggest that LOCC scores, which effectively rank genes based on their prognostic potential, represent a more suitable approach than ROC curves and Cox proportional hazard for prognostic modeling and understanding in cancer gene expression analysis. LOCC holds promise as an invaluable tool for advancing precision medicine and furthering biomarker research. Further research regarding multivariable integration and validation will help LOCC reach its full potential and establish its utility across diverse cancer types and clinical settings.

miRNA signatures underlie chemoresistance in the gemcitabine-resistant pancreatic ductal adenocarcinoma cell line MIA PaCa-2 GR

Introduction: Chemotherapy resistance remains a significant challenge in the treatment of pancreatic adenocarcinoma (PDAC), particularly in relation to gemcitabine (Gem), a commonly used chemotherapeutic agent. MicroRNAs (miRNAs) are known to influence cancer progression and chemoresistance. This study investigates the association between miRNA expression profiles and gemcitabine resistance in PDAC. Methods: The miRNA expression profiles of a gemcitabine-sensitive (GS) PDAC cell line, MIA PaCa-2, and its gemcitabine-resistant (GR) progeny, MIA PaCa-2 GR, were analyzed. miRNA sequencing (miRNA-seq) was employed to identify miRNAs expressed in these cell lines. Differential expression analysis was performed, and Ingenuity Pathway Analysis (IPA) was utilized to elucidate the biological functions of the differentially expressed miRNAs. Results: A total of 1867 miRNAs were detected across both cell lines. Among these, 97 (5.2%) miRNAs showed significant differential expression between the GR and GS cell lines, with 65 (3.5%) miRNAs upregulated and 32 (1.7%) miRNAs downregulated in the GR line. The most notably altered miRNAs were implicated in key biological processes such as cell proliferation, migration, invasion, chemosensitization, alternative splicing, apoptosis, and angiogenesis. A subset of these miRNAs was further analyzed in patient samples to identify potential markers for recurrent tumors. Discussion: The differential miRNA expression profiles identified in this study highlight the complex regulatory roles of miRNAs in gemcitabine resistance in PDAC. These findings suggest potential targets for improving prognosis and tailoring treatment strategies in PDAC patients, particularly those showing resistance to gemcitabine. Future research should focus on validating these miRNAs as biomarkers for resistance and exploring their therapeutic potential in overcoming chemoresistance.

A Systematic Review of Potential Biomarkers for Bacterial Burden and Treatment Efficacy Assessment in Tuberculosis Platform-Based Clinical Trials

Adaptive platform trials can be more efficient than classic trials for developing new treatments. Moving from culture-based to simpler- or faster-to-measure biomarkers as efficacy surrogates may enhance this advantage. We performed a systematic review of treatment efficacy biomarkers in adults with tuberculosis. Platform trials can span different development phases. We grouped biomarkers as: α, bacterial load estimates used in phase 2a trials; β, early and end-of treatment end points, phase 2b-c trials; γ, posttreatment or trial-level estimates, phase 2c-3 trials. We considered as analysis unit (biomarker entry) each combination of biomarker, predicted outcome, and their respective measurement times or intervals. Performance metrics included: sensitivity, specificity, area under the receiver-operator curve (AUC), and correlation measures, and classified as poor, promising, or good. Eighty-six studies included 22 864 participants. From 1356 biomarker entries, 318 were reported with the performance metrics of interest, with 103 promising and 41 good predictors. Group results were: α, mycobacterial RNA and lipoarabinomannan (LAM) in sputum, and host metabolites in urine; β, mycobacterial RNA and host transcriptomic or cytokine signatures for early treatment response; and γ, host transcriptomics for recurrence. A combination of biomarkers from different categories could help in designing more efficient platform trials. Efforts to develop efficacy surrogates should be better coordinated.

Thyroid nodule malignancy is associated with increased non-invasive hepatic fibrosis scores in metabolic subjects

Introduction

Thyroid cancer incidence is increasing, and adiposity-related conditions are gaining space in its pathogenesis. In this study, we aimed to detect any anthropometric, biohumoral, and clinical features that might be associated with thyroid nodule malignancy, potentially representing novel non-invasive markers of thyroid cancer.

Materials and methods

The study was conducted in a group of 142 consecutive outpatients (47 men and 95 women) who underwent fine-needle aspiration biopsy/cytology (FNAB/C) due to suspicion of malignancy from January 2018 to September 2022. We compared lipid and glycemic blood profiles as well as non-invasive liver fibrosis indexes such as aspartate aminotransferase (AST) to alanine aminotransferase (ALT) ratio (AAR), AST to platelet ratio index (APRI), and fibrosis index based on four factors (FIB-4) between patients with benign and malignant newly diagnosed nodules. Then, we performed receiver operating characteristic (ROC) analysis to assess their best cutoff values for discrimination of malignant nodules and chi-squared test to evaluate the association of specific dysmetabolic conditions with malignancy. To understand whether and to what degree dysmetabolic conditions increased the risk of thyroid nodule malignancy, we also calculated the odds ratio (OR) of the main biomarkers.

Results

After FNAB/C, 121 (85%) patients were diagnosed with benign thyroid nodules, while 21 (15%) individuals were diagnosed with thyroid cancer. Comparing patients with benign and malignant nodules, we found that individuals with thyroid cancer exhibited increased body mass index (BMI) (p = 0.048) and fasting plasma glucose (p = 0.046). Intriguingly, considering non-invasive scores for liver fibrosis, subjects with thyroid cancer presented increased AAR (p < 0.001) and APRI (p = 0.007), and these scores were associated with malignancy (p < 0.005) with OR = 7.1 and OR = 5, respectively. Moreover, we showed that only in the cancer group, low levels of vitamin D correlated with stigmata of impaired metabolism.

Discussion

In our study, AAR and APRI scores were associated with thyroid nodule malignancy and could be used to predict it and to speed up the diagnostic process. From a pathogenic point of view, we speculated that metabolic-associated fatty liver disease (MAFLD) along with hyperglycemia and vitamin D deficiency may represent putative drivers of thyroid carcinogenesis.

BMP4, SGSH, and SLC11A2 are Predicted to Be Biomarkers of Aging Associated with Programmed Cell Death

Most neurodegenerative diseases are exacerbated by aging, with symptoms often worsening over time. Programmed cell death (PCD) is a controlled cell suicide mechanism that is essential for the stability, growth, and homeostasis of organisms. Understanding the effects of aging at the level of systems biology could lead to new therapeutic approaches for a broad spectrum of neurodegenerative diseases. In the absence of comprehensive functional studies on the relationship between PCD and aging of the prefrontal cortex, this study provides prefrontal brain biomarkers of aging associated with PCD that could open the way for improved therapeutic techniques for age-related neurodegenerative diseases. To this end, publicly available transcriptome data were subjected to bioinformatic analyses such as differential gene expression, functional enrichment, and the weighted gene coexpression network analysis (WGCNA). The diagnostic utility of the biomarkers was tested using a logistic regression-based prediction model. Three genes, namely BMP4, SGSH, and SLC11A2, were found to be aging biomarkers associated with PCD. Finally, a multifactorial regulatory network with interacting proteins, transcription factors (TFs), competing endogenous RNAs (ceRNAs), and microRNAs (miRNAs) was constructed around these biomarkers. The elements of this multifactorial regulatory network were mainly enriched in BMP signaling. Further exploration of these three biomarkers and their regulatory elements would enable the development of 3PM (predictive, preventive, and personalized) medicine for the treatment of age-related neurodegenerative diseases.

A systematic review of predictive accuracy via c-statistic of preoperative frailty tests for extended length of stay, post-operative complications, and mortality

Frailty, as an age-related syndrome of reduced physiological reserve, contributes significantly to post-operative outcomes. With the aging population, frailty poses a significant threat to patients and health systems. Since 2012, preoperative frailty assessment has been recommended, yet its implementation has been inhibited by the vast number of frailty tests and lack of consensus. Since the anesthesiologist is the best placed for perioperative care, an anesthesia-tailored preoperative frailty test must be simple, quick, universally applicable to all surgeries, accurate, and ideally available in an app or online form. This systematic review attempted to rank frailty tests by predictive accuracy using the c-statistic in the outcomes of extended length of stay, 3-month post-operative complications, and 3-month mortality, as well as feasibility outcomes including time to completion, equipment and training requirements, cost, and database compatibility. Presenting findings of all frailty tests as a future reference for anesthesiologists, Clinical Frailty Scale was found to have the best combination of accuracy and feasibility for mortality with speed of completion and phone app availability; Edmonton Frailty Scale had the best accuracy for post-operative complications with opportunity for self-reporting. Finally, extended length of stay had too little data for recommendation of a frailty test. This review also demonstrated the need for changing research emphasis from odds ratios to metrics that measure the accuracy of a test itself, such as the c-statistic.

Unlocking the Potential of Circulating miRNAs in the Breast Cancer Neoadjuvant Setting: A Systematic Review and Meta-Analysis

The potential role of circulating microRNAs (miRNAs) as biomarkers in breast cancer (BC) management has been widely reported. However, the numerous discrepancies between studies in this regard hinders the implementation of circulating miRNAs in routine clinical practice. In the context of BC patients undergoing neoadjuvant chemotherapy (NAC), the possibility of predicting NAC response may lead to prognostic improvements by individualizing post-neoadjuvant therapy. In this context, the present meta-analysis aims to clarify circulating miRNAs' predictive role with respect to NAC response among BC patients. We conducted a comprehensive literature search on five medical databases until 16 February 2023. We pooled the effect sizes of each study by applying a random-effects model. Cochran's Q test (p-level of significance set at 0.05) scores and I² values were assessed to determine between-study heterogeneity. The PROBAST (Prediction Model Risk of Bias Assessment Tool) tool was used to evaluate the selected studies' risk of bias. Overall, our findings support the hypothesis that circulating miRNAs, specifically miR-21-5p and miR-155-5p, may act as predictive biomarkers in the neoadjuvant setting among BC patients. However, due to the limited number of studies included in this meta-analysis and the high degrees of clinical and statistical heterogeneity, further research is required to confirm the predictive power of circulating miR-21-5p and miR-155-5p.

LOCC: a novel visualization and scoring of cutoffs for continuous variables

Objective

There is a need for new methods to select and analyze cutoffs employed to define genes that are most prognostic significant and impactful. We designed LOCC (Luo's Optimization Categorization Curve), a novel tool to visualize and score continuous variables for a dichotomous outcome.

Methods

To demonstrate LOCC with real world data, we analyzed TCGA hepatocellular carcinoma gene expression and patient data using LOCC. We compared LOCC visualization to receiver operating characteristic (ROC) curve for prognostic modeling to showcase its utility in understanding predictors in various TCGA datasets.

Results

Analysis of E2F1 expression in hepatocellular carcinoma using LOCC demonstrated appropriate cutoff selection and validation. In addition, we compared LOCC visualization and scoring to ROC curves and c-statistics, demonstrating that LOCC better described predictors. Analysis of a previously published gene signature showed large differences in LOCC scoring, and removing the lowest scoring genes did not affect prognostic modeling of the gene signature demonstrating LOCC scoring could distinguish which predictors were most critical.

Conclusion

Overall, LOCC is a novel visualization tool for understanding and selecting cutoffs, particularly for gene expression analysis in cancer. The LOCC score can be used to rank genes for prognostic potential and is more suitable than ROC curves for prognostic modeling.

Sonicate Fluid Cellularity Predicted by Transcriptomic Deconvolution Differentiates Infectious from Non-Infectious Arthroplasty Failure

BACKGROUND

Although cellularity is traditionally assessed morphologically, deep sequencing approaches being used for microorganism detection may be able to provide information about cellularity. We hypothesized that cellularity predicted using CIBERSORTx (Stanford University), a transcriptomic-based cellular deconvolution tool, would differentiate between infectious and non-infectious arthroplasty failure.

METHODS

CIBERSORTx-derived cellularity profiles of 93 sonicate fluid samples, including 53 from subjects who underwent failed arthroplasties due to periprosthetic joint infection (PJI) (abbreviated for the purpose of this study as PJIF) and 40 from subjects who had undergone non-infectious arthroplasty failure (abbreviated NIAF) that had been subjected to bulk RNA sequencing were evaluated.

RESULTS

Samples from PJIF and NIAF subjects were differentially clustered by principal component analysis based on the cellularity profile. Twelve of the 22 individual predicted cellular fractions were differentially expressed in the PJIF cases compared with the NIAF cases, including increased predicted neutrophils (mean and standard error, 9.73% ± 1.06% and 0.81% ± 0.60%), activated mast cells (17.12% ± 1.51% and 4.11% ± 0.44%), and eosinophils (1.96% ± 0.37% and 0.42% ± 0.21%), and decreased predicted M0 macrophages (21.33% ± 1.51% and 39.75% ± 2.45%), M2 macrophages (3.56% ± 0.52% and 8.70% ± 1.08%), and regulatory T cells (1.57% ± 0.23% and 3.20% ± 0.34%). The predicted total granulocyte fraction was elevated in the PJIF cases (32.97% ± 2.13% and 11.76% ± 1.61%), and the samples from the NIAF cases had elevated predicted total macrophage and monocyte (34.71% ± 1.71% and 55.34% ± 2.37%) and total B cell fractions (5.89% ± 0.30% and 8.62% ± 0.86%). Receiver operating characteristic curve analysis identified predicted total granulocytes, neutrophils, and activated mast cells as highly able to differentiate between the PJIF cases and the NIAF cases. Within the PJIF cases, the total granulocyte, total macrophage and monocyte, M0 macrophage, and M2 macrophage fractions were differentially expressed in Staphylococcus aureus compared with Staphylococcus epidermidis -associated samples. Within the NIAF cases, the predicted total B cell, naïve B cell, plasma cell, and M2 macrophage fractions were differentially expressed among different causes of failure.

CONCLUSIONS

CIBERSORTx can predict the cellularity of sonicate fluid using transcriptomic data, allowing for the evaluation of the underlying immune response during the PJIF and NIAF cases, without a need to phenotypically assess cell composition.

Pre-hospital prediction of adverse outcomes in patients with suspected COVID-19: Development, application and comparison of machine learning and deep learning methods

BACKGROUND

COVID-19 infected millions of people and increased mortality worldwide. Patients with suspected COVID-19 utilised emergency medical services (EMS) and attended emergency departments, resulting in increased pressures and waiting times. Rapid and accurate decision-making is required to identify patients at high-risk of clinical deterioration following COVID-19 infection, whilst also avoiding unnecessary hospital admissions. Our study aimed to develop artificial intelligence models to predict adverse outcomes in suspected COVID-19 patients attended by EMS clinicians.

METHOD

Linked ambulance service data were obtained for 7,549 adult patients with suspected COVID-19 infection attended by EMS clinicians in the Yorkshire and Humber region (England) from 18-03-2020 to 29-06-2020. We used support vector machines (SVM), extreme gradient boosting, artificial neural network (ANN) models, ensemble learning methods and logistic regression to predict the primary outcome (death or need for organ support within 30 days). Models were compared with two baselines: the decision made by EMS clinicians to convey patients to hospital, and the PRIEST clinical severity score.

RESULTS

Of the 7,549 patients attended by EMS clinicians, 1,330 (17.6%) experienced the primary outcome. Machine Learning methods showed slight improvements in sensitivity over baseline results. Further improvements were obtained using stacking ensemble methods, the best geometric mean (GM) results were obtained using SVM and ANN as base learners when maximising sensitivity and specificity.

CONCLUSIONS

These methods could potentially reduce the numbers of patients conveyed to hospital without a concomitant increase in adverse outcomes. Further work is required to test the models externally and develop an automated system for use in clinical settings.

Pre-Diagnostic Circulating Resistin Concentrations Are Not Associated with Colorectal Cancer Risk in the European Prospective Investigation into Cancer and Nutrition Study

Resistin is a polypeptide implicated in inflammatory processes, and as such could be linked to colorectal carcinogenesis. In case-control studies, higher resistin levels have been found in colorectal cancer (CRC) patients compared to healthy individuals. However, evidence for the association between pre-diagnostic resistin and CRC risk is scarce. We investigated pre-diagnostic resistin concentrations and CRC risk within the European Prospective Investigation into Cancer and Nutrition using a nested case-control study among 1293 incident CRC-diagnosed cases and 1293 incidence density-matched controls. Conditional logistic regression models controlled for matching factors (age, sex, study center, fasting status, and women-related factors in women) and potential confounders (education, dietary and lifestyle factors, body mass index (BMI), BMI-adjusted waist circumference residuals) were used to estimate relative risks (RRs) and 95% confidence intervals (CIs) for CRC. Higher circulating resistin concentrations were not associated with CRC (RR per doubling resistin, 1.11; 95% CI 0.94-1.30; p = 0.22). There were also no associations with CRC subgroups defined by tumor subsite or sex. However, resistin was marginally associated with a higher CRC risk among participants followed-up maximally two years, but not among those followed-up after more than two years. We observed no substantial correlation between baseline circulating resistin concentrations and adiposity measures (BMI, waist circumference), adipokines (adiponectin, leptin), or metabolic and inflammatory biomarkers (C-reactive protein, C-peptide, high-density lipoprotein cholesterol, reactive oxygen metabolites) among controls. In this large-scale prospective cohort, there was little evidence of an association between baseline circulating resistin concentrations and CRC risk in European men and women.

A 92 protein inflammation panel performed on sonicate fluid differentiates periprosthetic joint infection from non-infectious causes of arthroplasty failure

Periprosthetic joint infection (PJI) is a major complication of total joint arthroplasty, typically necessitating surgical intervention and prolonged antimicrobial therapy. Currently, there is no perfect assay for PJI diagnosis. Proteomic profiling of sonicate fluid has the potential to differentiate PJI from non-infectious arthroplasty failure (NIAF) and possibly clinical subsets of PJI and/or NIAF. In this study, 200 sonicate fluid samples, including 90 from subjects with NIAF (23 aseptic loosening, 35 instability, 10 stiffness, five osteolysis, and 17 other) and 110 from subjects with PJI (40 Staphylococcus aureus, 40 Staphylococcus epidermidis, 10 Staphylococcus lugdunensis, 10 Streptococcus agalactiae, and 10 Enterococcus faecalis) were analyzed by proximity extension assay using the 92 protein Inflammation Panel from Olink Proteomics. Thirty-seven of the 92 proteins examined, including CCL20, OSM, EN-RAGE, IL8, and IL6, were differentially expressed in PJI versus NIAF sonicate fluid samples, with none of the 92 proteins differentially expressed between staphylococcal versus non-staphylococcal PJI, nor between the different types of NIAF studied. IL-17A and CCL11 were differentially expressed between PJI caused by different bacterial species, with IL-17A detected at higher levels in S. aureus compared to S. epidermidis and S. lugdunensis PJI, and CCL11 detected at higher levels in S. epidermidis compared to S. aureus and S. agalactiae PJI. Receiver operative characteristic curve analysis identified individual proteins and combinations of proteins that could differentiate PJI from NIAF. Overall, proteomic profiling using this small protein panel was able to differentiate between PJI and NIAF sonicate samples and provide a better understanding of the immune response during arthroplasty failure.

Novel Functional, Health, and Genetic Determinants of Cognitive Terminal Decline: Kuakini Honolulu Heart Program/Honolulu-Asia Aging Study

To investigate interindividual differences in cognitive terminal decline and identify determinants including functional, health, and genetic risk and protective factors, data from the Honolulu Heart Program/Honolulu-Asia Aging Study, a prospective cohort study of Japanese American men, were analyzed. The sample was recruited in 1965-1968 (ages 45-68 years). Longitudinal performance of cognitive abilities and mortality status were assessed from Exam 4 (1991-1993) through June 2014. Latent class analysis revealed 2 groups: maintainers retained relatively high levels of cognitive functioning until death and decliners demonstrated significant cognitive waning several years prior to death. Maintainers were more likely to have greater education, diagnosed coronary heart disease, and presence of the apolipoprotein E (APOE) ε2 allele and FOXO3 G allele (SNP rs2802292). Decliners were more likely to be older and have prior stroke, Parkinson's disease, dementia, and greater depressive symptoms at Exam 4, and the APOE ε4 allele. Findings support terminal decline using distance to death as the basis for modeling change. Significant differences were observed between maintainers and decliners 15 years prior to death, a finding much earlier compared to the majority of previous investigations.

Mucopolysaccharidosis Type IVA: Extracellular Matrix Biomarkers in Cardiovascular Disease

Cardiovascular disease (CVD) in Mucopolysaccharidosis Type IVA (Morquio A), signified by valvular disease and cardiac hypertrophy, is the second leading cause of death and remains untouched by current therapies. Enzyme replacement therapy (ERT) is the gold-standard treatment for MPS disorders including Morquio A. Early administration of ERT improves outcomes of patients from childhood to adulthood while posing new challenges including prognosis of CVD and ERT's negligible effect on cardiovascular health. Thus, having accurate biomarkers for CVD could be critical. Here we show that cathepsin S (CTSS) and elastin (ELN) can be used as biomarkers of extracellular matrix remodeling in Morquio A disease. We found in a cohort of 54 treatment naïve Morquio A patients and 74 normal controls that CTSS shows promising attributes as a biomarker in young Morquio A children. On the other hand, ELN shows promising attributes as a biomarker in adolescent and adult Morquio A. Plasma/urine keratan sulfate (KS), and urinary glycosaminoglycan (GAG) levels were significantly higher in Morquio A patients (p < 0.001) which decreased with age of patients. CTSS levels did not correlate with patients' phenotypic severity but differed significantly between patients (median range 5.45-8.52 ng/mL) and normal controls (median range 9.61-15.9 ng/mL; p < 0.001). We also studied α -2-macroglobulin (A2M), C-reactive protein (CRP), and circulating vascular cell adhesion molecule-1 (sVCAM-1) in a subset of samples to understand the relation between ECM biomarkers and the severity of CVD in Morquio A patients. Our experiments revealed that CRP and sVCAM-1 levels were lower in Morquio A patients compared to normal controls. We also observed a strong inverse correlation between urine/plasma KS and CRP (p = 0.013 and p = 0.022, respectively) in Morquio A patients as well as a moderate correlation between sVCAM-1 and CTSS in Morquio A patients at all ages (p = 0.03). As the first study to date investigating CTSS and ELN levels in Morquio A patients and in the normal population, our results establish a starting point for more elaborate studies in larger populations to understand how CTSS and ELN levels correlate with Morquio A severity.

Dysregulation of macrophage PEPD in obesity determines adipose tissue fibro-inflammation and insulin resistance

Resulting from impaired collagen turnover, fibrosis is a hallmark of adipose tissue (AT) dysfunction and obesity-associated insulin resistance (IR). Prolidase, also known as peptidase D (PEPD), plays a vital role in collagen turnover by degrading proline-containing dipeptides but its specific functional relevance in AT is unknown. Here we show that in human and mouse obesity, PEPD expression and activity decrease in AT, and PEPD is released into the systemic circulation, which promotes fibrosis and AT IR. Loss of the enzymatic function of PEPD by genetic ablation or pharmacological inhibition causes AT fibrosis in mice. In addition to its intracellular enzymatic role, secreted extracellular PEPD protein enhances macrophage and adipocyte fibro-inflammatory responses via EGFR signalling, thereby promoting AT fibrosis and IR. We further show that decreased prolidase activity is coupled with increased systemic levels of PEPD that act as a pathogenic trigger of AT fibrosis and IR. Thus, PEPD produced by macrophages might serve as a biomarker of AT fibro-inflammation and could represent a therapeutic target for AT fibrosis and obesity-associated IR and type 2 diabetes.

Biomarker Development in Cardiology: Reviewing the Past to Inform the Future

Cardiac biomarkers have become pivotal to the clinical practice of cardiology, but there remains much to discover that could benefit cardiology patients. We review the discovery of key protein biomarkers in the fields of acute coronary syndrome, heart failure, and atherosclerosis, giving an overview of the populations they were studied in and the statistics that were used to validate them. We review statistical approaches that are currently in use to assess new biomarkers and overview a framework for biomarker discovery and evaluation that could be incorporated into clinical trials to evaluate cardiovascular outcomes in the future.

Prediction of mortality among patients with chronic kidney disease: A systematic review

BACKGROUND

Chronic kidney disease (CKD) is a common medical condition that is increasing in prevalence. Existing published evidence has revealed through regression analyses that several clinical characteristics are associated with mortality in CKD patients. However, the predictive accuracies of these risk factors for mortality have not been clearly demonstrated.

AIM

To demonstrate the accuracy of mortality predictive factors in CKD patients by utilizing the area under the receiver operating characteristic (ROC) curve (AUC) analysis.

METHODS

We searched Ovid MEDLINE, EMBASE, and the Cochrane Library for eligible articles through January 2021. Studies were included based on the following criteria: (1) Study nature was observational or conference abstract; (2) Study populations involved patients with non-transplant CKD at any CKD stage severity; and (3) Predictive factors for mortality were presented with AUC analysis and its associated 95% confidence interval (CI). AUC of 0.70-0.79 is considered acceptable, 0.80-0.89 is considered excellent, and more than 0.90 is considered outstanding.

RESULTS

Of 1759 citations, a total of 18 studies (n = 14579) were included in this systematic review. Eight hundred thirty two patients had non-dialysis CKD, and 13747 patients had dialysis-dependent CKD (2160 patients on hemodialysis, 370 patients on peritoneal dialysis, and 11217 patients on non-differentiated dialysis modality). Of 24 mortality predictive factors, none were deemed outstanding for mortality prediction. A total of seven predictive factors [N-terminal pro-brain natriuretic peptide (NT-proBNP), BNP, soluble urokinase plasminogen activator receptor (suPAR), augmentation index, left atrial reservoir strain, C-reactive protein, and systolic pulmonary artery pressure] were identified as excellent. Seventeen predictive factors were in the acceptable range, which we classified into the following subgroups: predictors for the non-dialysis population, echocardiographic factors, comorbidities, and miscellaneous.

CONCLUSION

Several factors were found to predict mortality in CKD patients. Echocardiography is an important tool for mortality prognostication in CKD patients by evaluating left atrial reservoir strain, systolic pulmonary artery pressure, diastolic function, and left ventricular mass index.

A mixed effects model for analyzing area under the curve of longitudinally measured biomarkers with missing data

A simple approach for analyzing longitudinally measured biomarkers is to calculate summary measures such as the area under the curve (AUC) for each individual and then compare the mean AUC between treatment groups using methods such as t test. This two-step approach is difficult to implement when there are missing data since the AUC cannot be directly calculated for individuals with missing measurements. Simple methods for dealing with missing data include the complete case analysis and imputation. A recent study showed that the estimated mean AUC difference between treatment groups based on the linear mixed model (LMM), rather than on individually calculated AUCs by simple imputation, has negligible bias under random missing assumptions and only small bias when missing is not at random. However, this model assumes the outcome to be normally distributed, which is often violated in biomarker data. In this paper, we propose to use a LMM on log-transformed biomarkers, based on which statistical inference for the ratio, rather than difference, of AUC between treatment groups is provided. The proposed method can not only handle the potential baseline imbalance in a randomized trail but also circumvent the estimation of the nuisance variance parameters in the log-normal model. The proposed model is applied to a recently completed large randomized trial studying the effect of nicotine reduction on biomarker exposure of smokers.

A multi-platform metabolomics approach to identify possible biomarkers for human faecal contamination in Greenshell™ mussels (Perna canaliculus)

Bivalve molluscs have the potential to bioaccumulate microbial pathogens including noroviruses from aquatic environments and as such, there is a need for a rapid and cheap in-situ method for their detection. Here, we characterise the tissue-specific response of New Zealand Greenshell™ mussels (Perna canaliculus) to faecal contamination from two different sources (municipal sewage and human faeces). This is done with the view to identify potential biomarkers that could be further developed into low cost, rapid and sensitive in-situ biosensors for human faecal contamination detection of mussels in growing areas. Tissue-specific metabolic profiles from gills, haemolymph and digestive glands were analysed using gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS). Clear differentiation of metabolic profiles was observed among treatments in each tissue type. Overall, energy pathways such as glycolysis, citrate cycle and oxidative phosphorylation were downregulated across the three mussel tissues studied following simulated contamination events. Conversely, considerable sterol upregulation in the gills was observed after exposure to contamination. Additionally, free pools of nucleotide phosphates and the antioxidant glutathione declined considerably post-exposure to contamination in gills. These results provide important insights into the tissue-specific metabolic effects of human faecal contamination in mussels. This study demonstrates the utility of metabolomics as a tool for identifying potential biomarkers in mussels.

Evaluation of changes in intestinal microbiota in Crohn's disease patients after anti-TNF alpha treatment

Intestinal dysbiosis is key in the onset and development of Crohn's disease (CD). We evaluated the microbiota changes in CD patients before and after a six-month anti-TNF treatment, comparing these changes with the microbiota of healthy subjects. This prospective multicenter observational study involved 27 CD patients initiating anti-TNF treatment and 16 healthy individuals. Inflammatory activity was determined at baseline, 3 and 6 months, classifying patients into responders and non-responders. Fecal microbiota was analyzed by massive genomic sequencing thought 16S rRNA amplicon sequencing before and after six months of anti-TNF treatment. The CD cohort showed a decrease in genera of the class Clostridia, short-chain fatty acid producers, and an increase in the phylum Proteobacteria (p < 0.01) versus the healthy cohort. After anti-TNF treatment, the phylum Proteobacteria also increased in non-responders versus responders (13/27) (p < 0.005), with the class Clostridia increasing. In addition, alpha diversity increased in responders versus non-responders (p < 0.01), tending towards eubiosis. An association was found (p < 0.001) in the F.prausnitzii/E.coli ratio between responders and non-responders. The F/E ratio was the most accurate biomarker of anti-TNF response (area under the curve 0.87). Thus, anti-TNF treatment allows partial restoration of intestinal microbiota in responders and the F.prausnitzii/E.coli ratio can provide a reliable indicator of response to anti-TNF in CD.

Plasma Metabolites Alert Patients With Chest Pain to Occurrence of Myocardial Infarction

Myocardial infarction (MI) is one of the leading causes of death worldwide, and knowing the early warning signs of MI is lifesaving. To expand our knowledge of MI, we analyzed plasma metabolites in MI and non-MI chest pain cases to identify markers for alerting about MI occurrence based on metabolomics. A total of 230 volunteers were recruited, consisting of 146 chest pain patients admitted with suspected MI (85 MIs and 61 non-MI chest pain cases) and 84 control individuals. Non-MI cardiac chest pain cases include unstable angina (UA), myocarditis, valvular heart diseases, etc. The blood samples of all suspected MI cases were collected not longer than 6 h since the onset of chest pain. Gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry were applied to identify and quantify the plasma metabolites. Multivariate statistical analysis was utilized to analyze the data, and principal component analysis showed MI could be clearly distinguished from non-MI chest pain cases (including UA and other cases) in the scores plot of metabolomic data, better than that based on the data constructed with medical history and clinical biochemical parameters. Pathway analysis highlighted an upregulated methionine metabolism and downregulated arginine biosynthesis in MI cases. Receiver operating characteristic curve (ROC) and adjusted odds ratio (OR) were calculated to evaluate potential markers for the diagnosis and prediction ability of MI (MI vs. non-MI cases). Finally, gene expression profiles from the Gene Expression Omnibus (GEO) database were briefly discussed to study differential metabolites' connection with plasma transcriptomics. Deoxyuridine (dU), homoserine, and methionine scored highly in ROC analysis (AUC > 0.91), sensitivity (>80%), and specificity (>94%), and they were correlated to LDH and AST (p < 0.05). OR values suggested, after adjusting for gender, age, lipid levels, smoking, type II diabetes, and hypertension history, that high levels of dU of positive logOR = 3.01, methionine of logOR = 3.48, and homoserine of logOR = 1.61 and low levels of isopentenyl diphosphate (IDP) of negative logOR = -5.15, uracil of logOR = -2.38, and arginine of logOR = -0.82 were independent risk factors of MI. Our study highlighted that metabolites belonging to pyrimidine, methionine, and arginine metabolism are deeply influenced in MI plasma samples. dU, homoserine, and methionine are potential markers to recognize MI cases from other cardiac chest pain cases after the onset of chest pains. Individuals with high plasma abundance of dU, homoserine, or methionine have increased risk of MI, too.

Effect of parental smoking on their children's urine cotinine level in Korea: A population-based study

BACKGROUND

Children may be exposed to tobacco products in multiple ways if their parents smoke. The risks of exposure to secondhand smoke (SHS) are well known. This study aimed to investigate the association between parental smoking and the children's cotinine level in relation to restricting home smoking, in Korea.

METHODS

Using the Korea National Health and Nutrition Health Examination Survey data from 2014 to 2017, we analyzed urine cotinine data of parents and their non-smoking children (n = 1,403), in whose homes parents prohibited smoking. We performed linear regression analysis by adjusting age, sex, house type, and household income to determine if parent smoking was related to the urine cotinine concentration of their children. In addition, analysis of covariance and Tukey's post-hoc tests were performed according to parent smoking pattern.

FINDING

Children's urine cotinine concentrations were positively associated with those of their parents. Children of smoking parents had a significantly higher urine cotinine concentration than that in the group where both parents are non-smokers (diff = 0.933, P < .0001); mothers-only smoker group (diff = 0.511, P = 0.042); and fathers-only smoker group (diff = 0.712, P < .0001). In the fathers-only smoker group, the urine cotinine concentration was significantly higher than that in the group where both parents were non-smoker (diff = 0.221, P < .0001), but not significantly different compared to the mothers-only smoker group (diff = - -0.201, P = 0.388). Children living in apartments were more likely to be exposed to smoking substances.

CONCLUSION

This study showed a correlation between parents' and children's urine cotinine concentrations, supporting the occurrence of home smoking exposure due to the parents' smoking habit in Korea. Although avoiding indoor home smoking can decrease the children's exposure to tobacco, there is a need to identify other ways of smoking exposure and ensure appropriate monitoring and enforcement of banning smoking in the home.

Relationship between metalloproteinase-2 and -9 levels in plasma and vaginal secretion with preterm birth

OBJECTIVE

To study the association of plasma and vaginal levels of matrix metalloproteinases 2 (MMP-2) and 9 (MMP-9), determined between 20⁺⁰ and 25⁺⁶ weeks of gestation, with preterm birth and its predictive value.

STUDY DESIGN

All 129 cases of preterm birth (cases) and 258 randomly selected term patients (control group) were included in a cohort study with a nested case-control design. Inclusion criteria were living in Ribeirão Preto, Brazil, and gestational age 20⁺⁰ to 25⁺⁶ at enrollment. Exclusion criteria were multiple fetuses, congenital malformations or chromosome syndromes, and loss to follow-up. Maternal age, parity, smoking, and previous preterm birth were included as covariates. A blood sample and vaginal secretion were obtained for the determination of MMP-2 and MMP-9; the patients were screened for urinary tract infection and bacterial vaginosis, and cervical length was measured by ultrasound. The cut-off values for matrix metalloproteinases were calculated using receiver operating characteristic (ROC) curves for logistic regression analysis (crude and adjusted odds ratios).

RESULTS

According to the WHO, in this study, preterm subtypes included 3.8 % extremely preterm, 6.9 % very preterm, and 89.2 % late preterm births. The plasma MMP-9 cut-off was 63.25 ng/mL and the area under the ROC curve was 0.725 (standard error 0.03; 95 % confidence interval, 0.677-0.769). The cut-off for plasma MMP-2 was 239.4 ng/mL and the area under the ROC curve was 0.585 (standard error 0.03, 95 % confidence interval, 0.521-0.649). Crude odds ratios showed an increased risk of preterm birth associated with plasma MMP-2 (odds ratio, 1.75; 95 % confidence interval, 1.14-2.68) and plasma MMP-9 (odds ratio, 3.26, 95 % confidence interval, 2.09-5.07); no association was detected for vaginal MMP-2 or 9. For plasma, adjusted odds ratios were 1.42 (95 % confidence interval, 0.80-2.53) for MMP-2 and 2.71 (95 % confidence interval, 1 .52-4.83) for MMP-9, along with an increased risk in other covariates.

CONCLUSION

Elevated plasma MMP-9 levels and decreased MMP-2 levels were positively associated with preterm birth. Plasma MMP-9 level increased nearly three times the preterm risk.

Involvement of cytotoxic Eomes-expressing CD4+ T cells in secondary progressive multiple sclerosis

Multiple sclerosis (MS), a putative autoimmune disease of the central nervous system (CNS), commonly presents as relapsing-remitting MS (RRMS), characterized by recurrent episodes of peripheral disabling symptoms resulting from inflammatory CNS damage. Many RRMS patients transition to a chronic disease course with progressive neurological dysfunctions (secondary progressive MS, SPMS), with the progression rate varying between patients and over time. SPMS pathogenesis is now linked to immune-cell-mediated processes, although the mechanisms driving SPMS transition and progression remain elusive, and SPMS lacks biomarkers and effective treatments. We report the crucial involvement of cytotoxic CD4⁺ T cells expressing Eomes (Eomes⁺ Th cells) in SPMS pathogenesis-a Th cell subset previously identified in a mouse model of late/chronic autoimmune CNS inflammation. Few Eomes⁺ Th cells circulate in RRMS patient peripheral blood (n = 44), primary progressive MS (PPMS) patients (n = 25), or healthy controls (n = 42), but Eomes⁺ Th cells were significantly increased in SPMS (n = 105, P < 0.0001). Strikingly, lymphocytes isolated from SPMS autopsy brain samples revealed CD4⁺ T cells infiltrating CNS that coexpressed Eomes and the cytotoxic molecule granzyme B. In particular, the Eomes⁺ Th cell levels were increased in SPMS patients in progressive disease phases versus SPMS patients without current disability increases (P < 0.0001). Moreover, Eomes level acted as a biomarker to predict SPMS patients at risk of disease worsening with over 80% accuracy (ROC-AUC = 0.8276). Overall, our results indicate that granzyme B-expressing Eomes⁺ T helper cells are involved in the pathogenesis of SPMS, with significant implications for SPMS biomarkers and therapeutic targets.

Differential Macrophage Responses in Affective Versus Non-Affective First-Episode Psychosis Patients

Increased innate immune activation and inflammation are common findings in psychotic and affective (mood) disorders such as schizophrenia (SCZ), bipolar disorder (BD), and major depressive disorder (MDD), including increased numbers and activation of monocytes and macrophages. These findings often differ depending on the disorder, for example, we previously found increases in circulating inflammatory cytokines associated with monocytes and macrophages in SCZ, while BD had increases in anti-inflammatory cytokines. Despite these differences, few studies have specifically compared immune dysfunction in affective versus non-affective psychotic disorders and none have compared functional monocyte responses across these disorders. To address this, we recruited 25 first episode psychosis (FEP) patients and 23 healthy controls (HC). FEP patients were further grouped based on the presence (AFF) or absence (NON) of mood disorder. We isolated peripheral blood mononuclear cells and cultured them for 1 week with M-CSF to obtain monocyte-derived macrophages. These cells were then stimulated for 24 h to skew them to inflammatory and alternative phenotypes, in order to identify differences in these responses. Following stimulation with LPS and LPS plus IFNγ, we found that macrophages from the NON-group had diminished inflammatory responses compared to both HC and AFF groups. Interestingly, when skewing macrophages to an alternative phenotype using LPS plus IL-4, the AFF macrophages increased production of inflammatory cytokines. Receiver operating curve analysis showed predictive power of inflammatory cytokine concentrations after LPS stimulation in the AFF group versus NON-group. Our results suggest dysfunctional monocyte responses in both affective and non-affective psychotic disorder, with varying types of immune dysfunction depending on the presence or absence of a mood component.

Autism spectrum disorder screening in preschools

LAY ABSTRACT

With professional training and regular opportunities to observe children interacting with their peers, preschool teachers are in a good position to notice children's autism spectrum disorder symptomatology. Yet even when a preschool teacher suspects that a child may have autism spectrum disorder, fear of false alarm may hold the teacher back from alerting the parents, let alone suggesting them to consider clinical assessment for the child. A valid and convenient screening tool can help preschool teachers make more informed and hence more confident judgment. We set out to develop a screening tool that capitalizes on peer interaction as a naturalistic "stress test" to identify children more likely than their peers to have autism spectrum disorder. A total of 304 3- to 4-year-olds were observed at school with an 84-item preliminary checklist; data-driven item reduction yielded a 13-item Classroom Observation Scale. The Classroom Observation Scale scores correlated significantly with Autism Diagnostic Observation Schedule-2 scores. To validate the scale, another 322 2- to 4-year-olds were screened using the Classroom Observation Scale. The screen-positive children and randomly selected typically developing peers were assessed for autism spectrum disorder 1.5 years later. The Classroom Observation Scale as used by teachers and researchers near preschool onset predicted autism spectrum disorder diagnoses 1.5 years later. This user-friendly 13-item Classroom Observation Scale enables teachers and healthcare workers with little or no clinical training to identify, with reliable and valid results, preschoolers more likely than their peers to have autism spectrum disorder.

Renal length z-score for the detection of dysfunction in children with solitary functioning kidney

AIM

To evaluate whether renal length z-scores predict renal dysfunction in children with a solitary functioning kidney (SFK).

METHODS

In a single-centre retrospective cohort of children with SFK, we correlated body mass index z-scores, extracellular volume and lean body mass to renal length z-scores. We grouped these z-scores to other markers of renal dysfunction (proteinuria, hypertension, extracellular volume and abnormal estimated glomerular function rate [eGFR]) and analysed renal length z-score with multivariate analysis, receiver-operated characteristics (ROC) plots and Youden's index to determine an appropriate cut-off.

RESULTS

111 children had a median follow-up 5.08 years, eGFR 80.8 mL/min/1.73 m² , and age at last follow-up 7.4 (3.8-13.4 years). The median renal length z-scores of those without any renal dysfunction (n = 37, 25.1%) were greater (+3.66, interquartile range 3.02-4.47) than those with renal dysfunction (median 3.11, interquartile range 1.76-4.11, P = .0107, Mann-Whitney test). Using a cut-off of z-score of >+1.911, the odds ratio for having no renal dysfunction was 0.07 (95% CI 0.002-0.459, P = .0010). However, accuracy of the renal length z-score was poor (ROC curve 0.6488).

CONCLUSION

In this cohort of children with SKF, using the renal length z-score as a biomarker of renal dysfunction at 7 years of age is not recommended.

Explainable Machine Learning for Early Assessment of COVID-19 Risk Prediction in Emergency Departments

Between January and October of 2020, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus has infected more than 34 million persons in a worldwide pandemic leading to over one million deaths worldwide (data from the Johns Hopkins University). Since the virus begun to spread, emergency departments were busy with COVID-19 patients for whom a quick decision regarding in- or outpatient care was required. The virus can cause characteristic abnormalities in chest radiographs (CXR), but, due to the low sensitivity of CXR, additional variables and criteria are needed to accurately predict risk. Here, we describe a computerized system primarily aimed at extracting the most relevant radiological, clinical, and laboratory variables for improving patient risk prediction, and secondarily at presenting an explainable machine learning system, which may provide simple decision criteria to be used by clinicians as a support for assessing patient risk. To achieve robust and reliable variable selection, Boruta and Random Forest (RF) are combined in a 10-fold cross-validation scheme to produce a variable importance estimate not biased by the presence of surrogates. The most important variables are then selected to train a RF classifier, whose rules may be extracted, simplified, and pruned to finally build an associative tree, particularly appealing for its simplicity. Results show that the radiological score automatically computed through a neural network is highly correlated with the score computed by radiologists, and that laboratory variables, together with the number of comorbidities, aid risk prediction. The prediction performance of our approach was compared to that that of generalized linear models and shown to be effective and robust. The proposed machine learning-based computational system can be easily deployed and used in emergency departments for rapid and accurate risk prediction in COVID-19 patients.

Muscle activity and kinematics show different responses to recurrent laryngeal nerve lesion in mammal swallowing

Understanding the interactions between neural and musculoskeletal systems is key to identifying mechanisms of functional failure. Mammalian swallowing is a complex, poorly understood motor process. Lesion of the recurrent laryngeal nerve, a sensory and motor nerve of the upper airway, results in airway protection failure (liquid entry into the airway) during swallowing through an unknown mechanism. We examined how muscle and kinematic changes after recurrent laryngeal nerve lesion relate to airway protection in eight infant pigs. We tested two hypotheses: 1) kinematics and muscle function will both change in response to lesion in swallows with and without airway protection failure, and 2) differences in both kinematics and muscle function will predict whether airway protection failure occurs in lesion and intact pigs. We recorded swallowing with high-speed videofluoroscopy and simultaneous electromyography of oropharyngeal muscles pre- and postrecurrent laryngeal nerve lesion. Lesion changed the relationship between airway protection and timing of tongue and hyoid movements. Changes in onset and duration of hyolaryngeal muscles postlesion were less associated with airway protection outcomes. The tongue and hyoid kinematics all predicted airway protection outcomes differently pre- and postlesion. Onset and duration of activity in only one infrahyoid and one suprahyoid muscle showed a change in predictive relationship pre- and postlesion. Kinematics of the tongue and hyoid more directly reflect changes in airway protections pre- and postlesion than muscle activation patterns. Identifying mechanisms of airway protection failure requires specific functional hypotheses that link neural motor outputs to muscle activation to specific movements.NEW & NOTEWORTHY Kinematic and muscle activity patterns of oropharyngeal structures used in swallowing show different patterns of response to lesion of the recurrent laryngeal nerve. Understanding how muscles act on structures to produce behavior is necessary to understand neural control.

Sex-related differences in contemporary biomarkers for heart failure: a review

The use of circulating biomarkers for heart failure (HF) is engrained in contemporary cardiovascular practice and provides objective information about various pathophysiological pathways associated with HF syndrome. However, biomarker profiles differ considerably among women and men. For instance, in the general population, markers of cardiac stretch (natriuretic peptides) and fibrosis (galectin‐3) are higher in women, whereas markers of cardiac injury (cardiac troponins) and inflammation (sST2) are higher in men. Such differences may reflect sex‐specific pathogenic processes associated with HF risk, but may also arise as a result of differences in sex hormone profiles and fat distribution. From a clinical perspective, sex‐related differences in biomarker levels may affect the objectivity of biomarkers in HF management because what is considered to be ‘normal’ in one sex may not be so in the other. The objectives of this review are, therefore: (i) to examine the sex‐specific dynamics of clinically relevant HF biomarkers in the general population, as well as in HF patients; (ii) to discuss the overlap between sex‐related and obesity‐related effects, and (iii) to identify knowledge gaps to stimulate research on sex‐related differences in HF.

Markers of Hypoxia Correlate with Histologic and Endoscopic Severity of Colitis in Inflammatory Bowel Disease

BACKGROUND

Inflammation results in significant shifts in tissue metabolism. Recent studies indicate that inflammation and hypoxia occur concomitantly. We examined whether circulating and tissue markers of hypoxia could serve as surrogate indicators of disease severity in adult and pediatric patients with inflammatory bowel disease (IBD).

METHODS

Serum and colonic biopsies were obtained from pediatric subjects with active IBD colitis and adult subjects with active and inactive ulcerative colitis, along with healthy non-colitis controls of all ages. Disease activity was evaluated by endoscopy and histopathology. Levels of serum hypoxia markers (macrophage inflammatory protein-3α [MIP-3α], vascular endothelial growth factor [VEGF], and erythropoietin [EPO]) were measured.

RESULTS

Children with active IBD colitis had higher levels of serum MIP-3α and VEGF compared to non-colitis controls (p<0.01 and p<0.05, respectively). In adult subjects with endoscopically active ulcerative colitis, serum MIP-3α and EPO were significantly elevated compared to non-colitis controls (both p<0.01). In parallel, analysis of colon tissue MIP-3α mRNA and protein in pediatric subjects revealed increased expression in those with IBD colitis compared to controls (p<0.05 and p<0.01 for mRNA and protein, respectively). Serum MIP-3α and VEGF significantly increased with histology grade.

CONCLUSION

Peripheral blood hypoxia markers may be useful indicators of disease activity for pediatric and adult IBD patients.

miR-130a and Tgfβ Content in Extracellular Vesicles Derived from the Serum of Subjects at High Cardiovascular Risk Predicts their In-Vivo Angiogenic Potential

Serum-derived extracellular vesicles (sEV) from healthy donors display in-vivo pro-angiogenic properties. To identify patients that may benefit from autologous sEV administration for pro-angiogenic purposes, sEV angiogenic capability has been evaluated in type 2 diabetic (T2DM) subjects (D), in obese individuals with (OD) and without (O) T2DM, and in subjects with ischemic disease (IC) (9 patients/group). sEV display different angiogenic properties in such cluster of individuals. miRNomic profile and TGFβ content in sEV were evaluated. We found that miR-130a and TGFβ content correlates with sEV in-vitro and in-vivo angiogenic properties, particularly in T2DM patients. Ingenuity Pathway Analysis (IPA) identified a number of genes as among the most significant miR-130a interactors. Gain-of-function experiments recognized homeoboxA5 (HOXA5) as a miR-130a specific target. Finally, ROC curve analyses revealed that sEV ineffectiveness could be predicted (Likelihood Ratio+ (LH+) = 3.3 IC 95% from 2.6 to 3.9) by comparing miR-130a and TGFβ content 'in Series'. We demonstrate that sEV from high cardiovascular risk patients have different angiogenic properties and that miR-130a and TGFβ sEV content predicts 'true ineffective sEVs'. These results provide the rationale for the use of these assays to identify patients that may benefit from autologous sEV administration to boost the angiogenetic process.

Towards early monitoring of chemotherapy-induced drug resistance based on single cell metabolomics: Combining single-probe mass spectrometry with machine learning

Despite the presence of methods evaluating drug resistance during chemotherapies, techniques, which allow for monitoring the degree of drug resistance in early chemotherapeutic stage from single cells in their native microenvironment, are still absent. Herein, we report an analytical approach that combines single cell mass spectrometry (SCMS) based metabolomics with machine learning (ML) models to address the existing challenges. Metabolomic profiles of live cancer cells (HCT-116) with different levels (i.e., no, low, and high) of chemotherapy-induced drug resistance were measured using the Single-probe SCMS technique. A series of ML models, including random forest (RF), artificial neural network (ANN), and penalized logistic regression (LR), were constructed to predict the degrees of drug resistance of individual cells. A systematic comparison of performance was conducted among multiple models, and the method validation was carried out experimentally. Our results indicate that these ML models, especially the RF model constructed on the obtained SCMS datasets, can rapidly and accurately predict different degrees of drug resistance of live single cells. With such rapid and reliable assessment of drug resistance demonstrated at the single cell level, our method can be potentially employed to evaluate chemotherapeutic efficacy in the clinic.

Circulating Serum MicroRNAs as Potential Diagnostic Biomarkers of Posttraumatic Stress Disorder: A Pilot Study

Posttraumatic stress disorder (PTSD) is a psychiatric disorder that can develop upon exposure to a traumatic event. While most people are able to recover promptly, others are at increased risk of developing PTSD. However, the exact underlying biological mechanisms of differential susceptibility are unknown. Identifying biomarkers of PTSD could assist in its diagnosis and facilitate treatment planning. Here, we identified serum microRNAs (miRNAs) of subjects that underwent a traumatic event and aimed to assess their potential to serve as diagnostic biomarkers of PTSD. Next-generation sequencing was performed to examine circulating miRNA profiles of 24 members belonging to the Dutch military cohort Prospective Research in Stress-Related Military Operations (PRISMO). Three groups were selected: "susceptible" subjects who developed PTSD after combat exposure, "resilient" subjects without PTSD, and nonexposed control subjects (N = 8 per group). Differential expression analysis revealed 22 differentially expressed miRNAs in PTSD subjects compared to controls and 1 in PTSD subjects compared to resilient individuals (after multiple testing correction and a log2 fold-change cutoff of ≥|1|). Weighted Gene Coexpression Network Analysis (WGCNA) identified a module of coexpressed miRNAs which could distinguish between the three groups. In addition, receiver operating characteristic curve analyses suggest that the miRNAs with the highest module memberships could have a strong diagnostic accuracy as reflected by high areas under the curves. Overall, the results of our pilot study suggest that serum miRNAs could potentially serve as diagnostic biomarkers of PTSD, both individually or grouped within a cluster of coexpressed miRNAs. Larger studies are now needed to validate and build upon these preliminary findings.

Differences between the effects of conventional cigarettes, e-cigarettes and dual product use on urine cotinine levels

INTRODUCTION

METHODS

RESULTS

CONCLUSIONS

Precision medicine in lupus nephritis: can biomarkers get us there?

Patients with systemic lupus erythematosus frequently develop lupus nephritis (LN), a condition that can lead to end-stage kidney disease. Multiple serum and urine biomarkers for LN have been proposed in recent years, yet none have become incorporated into clinical use. The majority of studies have been single center with significant variability in cohorts, assays, and sample storage, leading to inconclusive results. It has become clear that no single biomarker is likely to be sufficient to diagnose LN, identify flares, and define the response to therapy and prognosis. A more likely scenario is a panel of urine, serum, tissue, and genetic biomarkers. In this review, we summarize traditional and novel biomarkers and discuss how they may be utilized in order to bring precision medicine to clinical practice in LN.

Serum biomarker for diagnostic evaluation of pulmonary arterial hypertension in systemic sclerosis

Background

Systemic sclerosis-associated pulmonary arterial hypertension (SSc-PAH) is one of the leading causes of death in SSc. Identification of a serum-based proteomic diagnostic biomarker for SSc-PAH would allow for rapid non-invasive screening and could positively impact patient survival. Identification and validation of novel proteins could potentially facilitate the identification of SSc-PAH, and might also point to important protein mediators in pathogenesis.

Methods

Thirteen treatment-naïve SSc-PAH patients had serum collected at time of diagnosis and were used as the discovery cohort for the protein-expression biomarker. Two proteins, Midkine and Follistatin-like 3 (FSTL3) were then validated by enzyme-linked immunosorbent assays. Midkine and FSTL3 were tested in combination to identify SSc-PAH and were validated in two independent cohorts of SSc-PAH (n = 23, n = 11).

Results

Eighty-two proteins were found to be differentially regulated in SSc-PAH sera. Two proteins (Midkine and FSTL3) were also shown to be elevated in publicly available data and their expression was evaluated in independent cohorts. In the validation cohorts, the combination of Midkine and FSTL3 had an area under the receiver operating characteristic curve (AUC) of 0.85 and 0.92 with respective corresponding measures of sensitivity of 76% and 91%, and specificity measures of 76% and 80%.

Conclusions

These findings indicate that there is a clear delineation between overall protein expression in sera from SSc patients and those with SSc-PAH. The combination of Midkine and FSTL3 can serve as an SSc-PAH biomarker and are potential drug targets for this rare disease population.

Electronic supplementary material

The online version of this article (10.1186/s13075-018-1679-8) contains supplementary material, which is available to authorized users.

Anti-α-Gal antibodies detected by novel neoglycoproteins as a diagnostic tool for Old World cutaneous leishmaniasis caused byLeishmania major

Outbreaks of Old World cutaneous leishmaniasis (CL) have significantly increased due to the conflicts in the Middle East, with most of the cases occurring in resource-limited areas such as refugee settlements. The standard methods of diagnosis include microscopy and parasite culture, which have several limitations. To address the growing need for a CL diagnostic that can be field applicable, we have identified five candidate neoglycoproteins (NGPs): Galα (NGP3B), Galα(1,3)Galα (NGP17B), Galα(1,3)Galβ (NGP9B), Galα(1,6)[Galα(1,2)]Galβ (NGP11B), and Galα(1,3)Galβ(1,4)Glcβ (NGP1B) that are differentially recognized in sera from individuals withLeishmania majorinfection as compared with sera from heterologous controls. These candidates contain terminal, non-reducing α-galactopyranosyl (α-Gal) residues, which are known potent immunogens to humans. Logistic regression models found that NGP3B retained the best diagnostic potential (area under the curve from receiver-operating characteristic curve = 0.8). Our data add to the growing body of work demonstrating the exploitability of the human anti-α-Gal response in CL diagnosis.

Addition of Novel Biomarkers for Predicting All-Cause and Cardiovascular Mortality in Prevalent Hemodialysis Patients

Novel biomarkers might improve the prediction of mortality in hemodialysis (HD) patients. We simultaneously measured the levels of conventional and novel biomarkers [serum N-terminal pro-brain natriuretic peptide (NT-proBNP), intact fibroblast growth factor-23 (FGF23), β2-microglobulin (β2MG), cystatin C, and high-sensitivity C-reactive protein (hsCRP)] in 307 prevalent Japanese HD patients. There were 66 all-cause deaths, and 25 cardiovascular (CV) deaths during 2 years, which were assessed using Cox models and concordance (C)-statistics. The addition of NT-proBNP alone (P < 0.05) or NT-proBNP, hsCRP, and β2MG as a panel (C-statistics: 0.834 vs. 0.776, P < 0.01) to a conventional risk model composed of age, diabetes, and the serum albumin level significantly improved the prediction of 2-year all-cause mortality, and the addition of NT-proBNP and hsCRP as a panel to a conventional risk model composed of age significantly improved the prediction of 2-year CV mortality (P < 0.05) in Japanese prevalent HD patients. Neither FGF23 nor cystatin C improved mortality prediction.