Discriminatory Biomarkers Research Articles

MS analysis of the plasma metabolome reveals major changes of amino acid and energy metabolism for early-onset schizophrenia.

Early-onset schizophrenia (EOS) is a severe and chronic mental disease that manifests during childhood and adolescence. There are currently no objective biomarkers to diagnose this psychosis. Recent research has shown that metabolic disorders are closely associated with the onset of schizophrenia, but there is a lack of evidence among children and adolescent populations. This study will analyze the metabolic characteristics of patients with early-onset schizophrenia through plasma metabolomics. We analyzed plasma from 13 EOS patients and 15 healthy controls using ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-QTOF/MS) technology to identify potential biomarkers for EOS. The discriminative potential biomarkers in delineating EOS patients from controls. A total of 22 different metabolites were found to be effective in differentiating EOS patients from healthy controls. EOS patients demonstrated statistically significant differences compared to the healthy control group, with 6 metabolites registering lower levels and 16 metabolites showing higher levels (P < 0.05). The main metabolic pathways involved include arachidonic acid metabolism, histidine metabolism, non-natural amino acid metabolism, tryptophan metabolism, and metabolism of exogenous substances mediated by cytochrome P450. These metabolites suggest that disturbances in amino acid and energy metabolism may be involved in the pathogenesis of EOS. The findings provide important clues for further understanding the pathogenesis of EOS and offer potential biomarkers for the diagnosis and treatment of the disease.

SVM-DO: identification of tumor-discriminating mRNA signatures via support vector machines supported by Disease Ontology.

The complicated nature of tumor formation makes it difficult to identify discriminatory genes. Recently, transcriptome-based supervised classification methods using support vector machines (SVMs) have become popular in this field. However, the inclusion of less significant variables in the construction of classification models can lead to misclassification. To improve model performance, feature selection methods such as enrichment analysis can be used to extract useful variable sets. The detection of genes that can discriminate between normal and tumor samples in the association of cancer and disease remains an area of limited information. We therefore aimed to discover novel and practical sets of discriminatory biomarkers by utilizing the association of cancer and disease. In this study, we employed an SVM classification method for differentially expressed genes enriched by Disease Ontology and filtered nondiscriminatory features using Wilk's lambda criterion prior to classification. Our approach uses the discovery of disease-associated genes as a viable strategy to identify gene sets that discriminate between tumor and normal states. We analyzed the performance of our algorithm using comprehensive RNA-Seq data for adenocarcinoma of the colon, squamous cell carcinoma of the lung, and adenocarcinoma of the lung. The classification performance of the obtained gene sets was analyzed by comparison with different expression datasets and previous studies using the same datasets. It was found that our algorithm extracts stable small gene sets that provide high accuracy in predicting cancer status. In addition, the gene sets generated by our method perform well in survival analyses, indicating their potential for prognosis. By combining gene sets for both diagnosis and prognosis, our method can improve clinical applications in cancer research. Our algorithm is available as an R package with a graphical user interface in Bioconductor (https://doi.org/10.18129/B9.bioc.SVMDO) and GitHub (https://github.com/robogeno/SVMDO).

Impact of demographics and comorbid conditions on plasma biomarkers concentrations and their diagnostic accuracy in a memory clinic cohort.

Plasma biomarkers have emerged as promising tools for identifying amyloid beta (Aβ) pathology. Before implementation in routine clinical practice, confounding factors modifying their concentration beyond neurodegenerative diseases should be identified. We studied the association of a comprehensive list of demographics, comorbidities, medication and laboratory parameters with plasma p-tau181, glial fibrillary acidic protein (GFAP) and neurofilament light chain (NfL) on a prospective memory clinic cohort and studied their impact on diagnostic accuracy for discriminating CSF/amyloid PET-defined Aβ status. Three hundred sixty patients (mean age 66.5years, 55% females, 53% Aβ positive) were included. Sex, age and Aβ status-adjusted models showed that only estimated glomerular filtration rate (eGFR, standardized β -0.115 [-0.192 to -0.035], p = 0.005) was associated with p-tau181 levels, although with a much smaller effect than Aβ status (0.685 [0.607-0.763], p < 0.001). Age, sex, body mass index (BMI), Charlson comorbidity index (CCI) and eGFR significantly modified GFAP concentration. Age, blood volume (BV) and eGFR were associated with NfL levels. p-tau181 predicted Aβ status with 87% sensitivity and specificity with no relevant increase in diagnostic performance by adding any of the confounding factors. Using two cut-offs, plasma p-tau181 could have spared 62% of amyloid-PET/CSF testing. Excluding patients with chronic kidney disease did not change the proposed cut-offs nor the diagnostic performance. In conclusion, in a memory clinic cohort, age, sex, eGFR, BMI, BV and CCI slightly modified plasma p-tau181, GFAP and NfL concentrations but their impact on the diagnostic accuracy of plasma biomarkers for Aβ status discrimination was minimal.

A novel automated multiplex immunoassay for the ultrasensitive detection of blood‐based biomarkers for neurodegenerative diseases

AbstractBackgroundEfforts to identify accurate blood biomarkers for neurodegenerative disease hallmarks have been hampered by the lack of a proteomic tool that has the required sensitivity to detect very low concentrations of brain‐derived proteins in plasma or serum and the ability to multiplex many analytes in a single assay. Here we evaluated NULISA™, a recently developed novel immunoassay with attomolar level sensitivity and high multiplex capability1, for its ability to detect serum biomarkers associated with Alzheimer’s disease.MethodSerum samples from 31 Alzheimer’s disease (AD) patients and 31 non‐AD controls were analyzed using NULISA with a 200plex inflammation panel targeting a broad spectrum of inflammation‐related cytokines, chemokines and other proteins. Five samples failed quality control and were excluded from further analysis, resulting in 28 AD and 29 control samples in the final dataset. Linear model analysis, including age and sex as covariates, was performed for each target for differential expression between AD patients and controls.ResultTwo significant proteins were identified. The level of glial fibrillary acidic protein (GFAP) was 1.84‐fold higher in AD patients (adjusted p‐value = 0.00014), whereas the level of S100 calcium‐binding protein A12 (S100A12) was 2‐fold higher in non‐AD controls (adjusted p‐value = 0.031). GFAP serum levels correlated strongly with total tau (Spearman rho = 0.69, p = 1.7e‐09), p‐tau181 (rho = 0.69, p = 1.7e‐09) and amyloid beta 42 (rho = ‐0.71, p = 4.7e‐10) levels in cerebrospinal fluid (CSF). S100A12 levels also correlated significantly with total tau (rho = ‐0.39, p = 0.0031), p‐tau181 (rho = ‐0.36, p = 0.0053) and amyloid beta 42 (rho = 0.38, p = 0.0067) CSF levels. Receiver operating characteristic analysis demonstrated that GFAP strongly discriminated AD from controls with an area under the curve (AUC) of 0.93 (95% CI 0.87‐1.0), and S100A12 also exhibited good discrimination with an AUC of 0.72 (95% CI 0.59‐0.86).ConclusionThis exploratory study identified serum GFAP levels as a strong discriminatory biomarker for AD, consistent with previous studies. S100A12 also demonstrated significant associations with AD and CSF biomarkers and thus warrants further study. NULISA holds great promise for the discovery and validation of blood‐based biomarkers for the early detection and monitoring of neurodegenerative diseases.1. Feng, W. et al. bioRxiv 2023.04.09.536130 (2023) https://doi.org/10.1101/2023.04.09.536130.

Novel EEG tools to predict early cognitive impairments in Parkinson’s disease

AbstractBackgroundParkinson’s disease (PD) is a neurodegenerative disorder mainly characterised by motor symptoms but often also associated with dementia, increasing in likelihood with duration of disease. Electroencephalography (EEG) can aid in the early diagnosis of those at risk of developing dementia. Therefore, we analysed EEG recordings across conscious states from PD patients with and without cognitive symptoms (Latreille et al in 2016, https://doi.org/10.1093/brain/aww018).MethodBaseline data (REM sleep, NREM sleep, WAKE) from 38 PD patients (15 with and 23 without mild cognitive impairment) and 27 healthy subjects provided power spectra (qEEG), relative band power, as well as dominant frequency and its standard deviation to investigate markers for cognitive impairment. At follow‐up (an average 4.5 years after baseline), 11 patients developed dementia (PDD). Connectivity measures for left and right occipital and central electrodes were obtained as described in Crouch et al, 2018, https://doi.org/10.1038/s41598‐018‐19707‐1.ResultqEGG analyses confirmed previous reports of enhanced theta power during REM, NREM and WAKE for patients who developed PDD. A reduction in alpha power during WAKE was observed for MCI and PDD groups when compared to controls. Alpha rhythms generally decrease with age, alongside enhanced theta power, and this outcome may contribute to the global cognitive status of patients. A reduced and less variable dominant frequency was evident for MCI and PDD patients during WAKE, indicative of reduced flexibility of the network. Finally, rPDC detected disease‐specific changes, such as enhanced cross‐hemispheric connectivity in PDD in the alpha band during NREM sleep that indicate a lack of dynamism in the network. Also noted was the diminished interhemispheric communication in delta power between both central and occipital channels in PDD during WAKE.ConclusionTogether, we here confirm that changes of theta and alpha power are optimal discriminatory biomarkers for PD with cognitive decline (with MCI and at risk of dementia). Also, spectral and rPDC analyses indicate that dementia in PD is preceded by a loss of dynamism in EEG activity during MCI stages, both at the electrode and network level.

Evaluation of novel biomarkers for early pregnancy outcome prediction†.

To assess performance and discriminatory capacity of commercially available enzyme-linked immunosorbent assays of biomarkers for predicting first trimester pregnancy outcome in a multi-center cohort. In a case-control study at three academic centers of women with pain and bleeding in early pregnancy, enzyme-linked immunosorbent assays of biomarkers were screened for assay performance. Performance was assessed via functional sensitivity, assay reportable range, recovery/linearity, and intra-assay precision (%Coefficient of Variation). Top candidates were analyzed for discriminatory capacity for viability and location among 210 women with tubal ectopic pregnancy, viable intrauterine pregnancy, or miscarriage. Assay discrimination was assessed by visual plots, area under the curve with 95% confidence intervals, and measures of central tendency with two-sample t-tests. Of 25 biomarkers evaluated, 22 demonstrated good or acceptable assay performance. Transgelin-2, oviductal glycoprotein, and integrin-linked kinase were rejected due to poor performance. The best biomarkers for discrimination of pregnancy location were pregnancy-specific beta-1-glycoprotein 9, pregnancy-specific beta-1-glycoprotein 1, insulin-like growth factor binding protein 1, kisspeptin (KISS1), pregnancy-specific beta-1-glycoprotein 3, and beta parvin (PARVB). The best biomarkers for discrimination of pregnancy viability were pregnancy-specific beta-1-glycoprotein 9, pregnancy-specific beta-1-glycoprotein 3, EH domain-containing protein 3, KISS1, WAP four-disulfide core domain protein 2 (HE4), quiescin sulfhydryl oxidase 2, and pregnancy-specific beta-1-glycoprotein 1. Performance of commercially available enzyme-linked immunosorbent assays was acceptable for a panel of novel biomarkers to predict early pregnancy outcome. Of these, six and seven candidates demonstrated good discriminatory capacity of pregnancy location and viability, respectively, when validated in a distinct external population. Four markers demonstrated good discrimination for both location and viability.

Comparing plasma and skin imprint metabolic profiles in COVID-19 diagnosis and severity assessment.

As SARS-CoV-2 continues to produce new variants, the demand for diagnostics and a better understanding of COVID-19 remain key topics in healthcare. Skin manifestations have been widely reported in cases of COVID-19, but the mechanisms and markers of these symptoms are poorly described. In this cross-sectional study, 101 patients (64 COVID-19 positive patients and 37 controls) were enrolled between April and June 2020, during the first wave of COVID-19, in São Paulo, Brazil. Enrolled patients had skin imprints sampled non-invasively using silica plates; plasma samples were also collected. Samples were used for untargeted lipidomics/metabolomics through high-resolution mass spectrometry. We identified 558 molecular ions, with lipids comprising most of them. We found 245 plasma ions that were significant for COVID-19 diagnosis, compared to 61 from the skin imprints. Plasma samples outperformed skin imprints in distinguishing patients with COVID-19 from controls, with F1-scores of 91.9% and 84.3%, respectively. Skin imprints were excellent for assessing disease severity, exhibiting an F1-score of 93.5% when discriminating between patient hospitalization and home care statuses. Specifically, oleamide and linoleamide were the most discriminative biomarkers for identifying hospitalized patients through skin imprinting, and palmitic amides and N-acylethanolamine 18:0 were also identified as significant biomarkers. These observations underscore the importance of primary fatty acid amides and N-acylethanolamines in immunomodulatory processes and metabolic disorders. These findings confirm the potential utility of skin imprinting as a valuable non-invasive sampling method for COVID-19 screening; a method that may also be applied in the evaluation of other medical conditions. KEY MESSAGES: Skin imprints complement plasma in disease metabolomics. The annotated markers have a role in immunomodulation and metabolic diseases. Skin imprints outperformed plasma samples at assessing disease severity. Skin imprints have potential as non-invasive sampling strategy for COVID-19.

Ventricular volume ratio as a novel imaging biomarker for disease prediction in 26,516 UK Biobank participants

Abstract Background/Introduction Right and left ventricular volumes are established imaging biomarkers for disease discrimination and outcome prediction. Limited research in select disease cohorts suggests additional clinical value from consideration of disproportionate (or imbalanced) dilatation of one ventricle over the other. The prognostic utility of this "ventricular volume ratio" (VVR) has not been tested in large, unselected cohorts. Purpose To examine associations of right-to-left and left-to-right VVR with cardiovascular phenotypes, prevalent disease, and incident cardiac outcomes in the UK Biobank. Methods The analysis includes 26,516 participants with cardiac magnetic resonance (CMR) available. We calculated right-to-left ventricular volume ratio (RV/LV), and the reciprocal left-to-right ventricular ratio (LV/RV), and identified participants with asymmetry above the 95th percentile of healthy cases. Multivariable linear and logistic regression models were used to calculate associations between ventricular volume asymmetry and sample characteristics, other cardiac imaging features, existing disease and incident disease. Incident disease associations were measured using Cox proportional hazards models adjusted by age, sex, systolic blood pressure, body mass index, smoking, Townsend deprivation score, hypertension, diabetes, hyperlipidaemia and other key imaging metrics (LV mass indexed to body surface area, LV ejection fraction and RV ejection fraction). The average follow-up time was 4.6 years (SD 1.3 years). Results Ventricular volume asymmetry in either direction was associated with increased age, lower RVEF, poorer LV global function index, poorer global longitudinal strain and greater maximum wall thickness. While RV-dominant volume asymmetry was associated with increased risk of all-cause mortality (HR= 1.56, 95% CI= [1.08, 2.26], p=0.019), LV-dominant volume asymmetry was found to associate with a much wider array of factors in the UK Biobank population. LV dominance was significantly associated with smoking, hypertension, diabetes, hyperlipidaemia, existing respiratory disease and existing heart disease. In fully-adjusted models, LV dominance was associated with increased risk for ischaemic heart disease (1.46 [1.12, 1.91], p=0.005), non-ischaemic cardiomyopathies (3.62, [1.93, 6.79], p &amp;lt; 0.001) and a two-fold increased risk for heart failure (2.30, [1.63, 3.26], p &amp;lt; 0.001). Conclusion(s) LV/RV is easily calculated from existing imaging metrics, and could potentially have important added value as a biomarker in predicting heart disease, incremental to well-established risk factors and imaging metrics.

Bidirectional regulation effect of rhubarb as laxative and astringent by metabolomics studies

Ethnopharmacological relevanceRhubarb, a prominent traditional Chinese medicine, has been employed as a potent laxative for centuries and garnered particular popularity among the youth owing to its notable efficacy in weight management. Historical records indicated that rhubarb initially exhibited robust laxative properties, but extended and consistent usage may lead to an astringent response in the later stage of long-term use. In contrast, steamed pieces of rhubarb (SR), preparing through the process of steaming with wine, have demonstrated a gentle laxative effect with no reported adverse effects. Aim of the studyOur study was designed to explore the intricate mechanisms underlying laxative and astringent properties of rhubarb through metabolomics research. Materials and methodsIn this investigation, we employed a serum metabolomics approach utilizing the UPLC-Q-Extractive-Orbitrap-MS method to delve into the contrasting laxative and astringent effects of rhubarb, as well as to unravel the mechanisms of underpinning its bidirectional regulatory influence. To commence, we assessed alterations in Evacuation Index (EI) values, intestinal hormone levels, and colon histopathology in mice to gauge rhubarb's laxative and astringent effects. Subsequently, metabolomics methodology was employed for cluster analysis through Principal Component Analysis (PCA) and biomarker identification via Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA). Then, we delved into the distinctions in characteristic biomarkers, metabolic pathways, their association with pathological changes, and correlation heatmap for biomarkers between raw pieces of rhubarb (RR) and SR to gain insights into the potential mechanisms behind rhubarb's bidirectional regulatory effects. ResultsOur findings revealed that RR exhibited a laxative effect in the early stage and transitioned to an astringent effect in the later stage, as indicated by the EI values. In contrast, SR consistently demonstrated a mild laxative effect. Biochemical indexes and histopathological assessments unveiled that RR triggered its astringent effect by inhibiting secretion of motilin (MTL), promoting secretion of vasoactive intestinal peptide (VIP) and epinephrine (EPI), and inducing onset of inflammation. Furthermore, serum metabolomics analysis identified 59 discriminative biomarkers modulated by RR and SR. Through comprehensive analysis, we elucidated the in vivo transformation relationships among multiple endogenous metabolites. Notably, our results underscored the down-regulation of certain phosphatidylcholines (PCs), amino acids, acylcarnitines, and up-regulation of lysophosphatidylcholines (LysoPCs) played pivotal roles in the onset of gut dysfunction, intestinal inflammation, gut barrier damage, and gastrointestinal motility disorder upon prolonging RR administration, ultimately contributing to its astringent effect. Additionally, our correlation analysis elucidated that anthraquinones, stilbenes, and phenylbutanones were the pharmacodynamic material basis responsible for inducing the astringent effect of RR. ConclusionThis study provides valuable insights into the bidirectional regulatory effects of rhubarb and sheds light on its underlying mechanisms through a comprehensive metabolomics approach.

Exploring digital speech biomarkers of hypokinetic dysarthria in a multilingual cohort

Hypokinetic dysarthria, a motor speech disorder characterized by reduced movement and control in the speech-related muscles, is mostly associated with Parkinson’s disease. Acoustic speech features thus offer the potential for early digital biomarkers to diagnose and monitor the progression of this disease. However, the influence of language on the successful classification of healthy and dysarthric speech remains crucial. This paper explores the analysis of acoustic speech features, both established and newly proposed, in a multilingual context to support the diagnosis of PD. The study aims to identify language-independent and highly discriminative digital speech biomarkers using statistical analysis and machine learning techniques. The study analyzes thirty-three acoustic features extracted from Czech, American, Israeli, Columbian, and Italian PD patients, as well as healthy controls. The analysis employs correlation and statistical tests, descriptive statistics, and the XGBoost classifier. Feature importances and Shapley values are used to provide explanations for the classification results. The study reveals that the most discriminative features, with reduced language dependence, are those measuring the prominence of the second formant, monopitch, and the frequency of pauses during text reading. Classification accuracies range from 67% to 85%, depending on the language. This paper introduces the concept of language robustness as a desirable quality in digital speech biomarkers, ensuring consistent behaviour across languages. By leveraging this concept and employing additional metrics, the study proposes several language-independent digital speech biomarkers with high discrimination power for diagnosing PD.

Differential Cellular Interactome in Schizophrenia and Bipolar Disorder-Discriminatory Biomarker Role.

Schizophrenia (SCH) and bipolar disorder (BD) are two of the most important psychiatric pathologies due to their high population incidence and disabling power, but they also present, mainly in their debut, high clinical similarities that make their discrimination difficult. In this work, the differential oxidative stress, present in both disorders, is shown as a concatenator of the systemic alterations-both plasma and erythrocyte, and even at the level of peripheral blood mononuclear cells (PBMC)-in which, for the first time, the different affectations that both disorders cause at the level of the cellular interactome were observed. A marked erythrocyte antioxidant imbalance only present in SCH generalizes to oxidative damage at the plasma level and shows a clear impact on cellular involvement. From the alteration of protein synthesis to the induction of death by apoptosis, including proteasomal damage, mitochondrial imbalance, and autophagic alteration, all the data show a greater cellular affectation in SCH than in BD, which could be linked to increased oxidative stress. Thus, patients with SCH in our study show increased endoplasmic reticulum (ER)stress that induces increased proteasomal activity and a multifactorial response to misfolded proteins (UPR), which, together with altered mitochondrial activity, generating free radicals and leading to insufficient energy production, is associated with defective autophagy and ultimately leads the cell to a high apoptotic predisposition. In BD, however, oxidative damage is much milder and without significant activation of survival mechanisms or inhibition of apoptosis. These clear differences identified at the molecular and cellular level between the two disorders, resulting from progressive afflictions in which oxidative stress can be both a cause and a consequence, significantly improve the understanding of both disorders to date and are essential for the development of targeted and preventive treatments.

Biomarker Development for Identifying Mud Loach (Misgurnus mizolepis) Origin Country Using Untargeted Metabolite Profiling.

Mud loach (Misgurnus mizolepis) has long been consumed in Korea. Recently, Chinese mud loaches were replaced with expensive Korean mud loaches, owing to taste and preference. Such issues occur in aquatic food distribution processes, leading to inferior food delivery. Previously, a study was conducted to confirm the origin of mud loaches using genetic analysis. However, untargeted metabolites profiling of mud loaches has not been reported. Untargeted metabolomics provides information on the overall metabolic profiling of a sample, allowing the identification of new metabolites. Here, we analyzed the metabolites of mud loaches of different geographical origins using liquid chromatography (LC)-quadrupole-time-of-flight mass spectrometry (MS). Orthogonal partial least squares discriminant analysis from LC/MS datasets showed a clear distinction between Korean and Chinese mud loaches, and univariate statistical analysis showed significantly different metabolites between them. N-acetylhistidine and anserine were selected as biomarkers for geographical origin discrimination using the receiver operating characteristic curve. N-acetylhistidine and anserine levels were significantly higher in Chinese than in Korean mud loaches. These results indicate that metabolic analysis can be used to discriminate between the geographical origins of mud loaches, curtailing the inadvertent substitution of mud loaches from different regions.

Impact of Ocean Acidification on the Gut Histopathology and Intestinal Microflora of Exopalaemon carinicauda.

Marine crustaceans are severely threatened by environmental factors such as ocean acidification, but, despite the latter's negative impact on growth, molting, and immunity, its effects on intestinal microflora remain poorly understood. This work studied the gut morphology and intestinal microflora of Exopalaemon carinicauda, grown in seawater of different pH levels: 8.1 (control group), 7.4 (AC74 group), and 7.0 (AC70 group). Ocean acidification was found to cause intestinal damage, while significantly altering the microflora's composition. However, the α-diversity did not differ significantly between the groups. At the phylum level, the relative abundance of Proteobacteria decreased in the acidification groups, while at the genus level, the relative abundance of Sphingomonas decreased. Babeliales was a prominent discriminative biomarker in the AC74 group, with Actinobacteriota, Micrococcales, Beijerinckiaceae, Methylobacterium, and Flavobacteriales being the main ones in the AC70 group. The function prediction results also indicated an enrichment of pathways related to metabolism for the acidification groups. At the same time, those related to xenobiotics' biodegradation and metabolism were inhibited in AC74 but enhanced in AC70. This is the first study examining the impact of ocean acidification on the intestinal microflora of crustaceans. The results are expected to provide a better understanding of the interactions between shrimp and their microflora in response to environmental stressors.

OR02-03 Twenty-four-hour Tissue Microdialysis Of Aldosterone And Metabolites In Primary Aldosteronism

Abstract Disclosure: M.A. Grytaas: None. P. Methlie: None. I. Marinelli: None. E. Zavala: None. M. Øksnes: None. T. Upton: None. K. Simunkova: None. D.A. Vassiliadi: None. K. Løvås: None. S. Bensing: None. I. Botusan: None. K. Berinder: None. G.M. Russell: None. G. Ueland: None. O. Kampe: None. S. Tsagarakis: None. S.L. Lightman: None. E.S. Husebye: None. Background: Primary aldosteronism (PA) is the most common cause of secondary hypertension but is grossly underdiagnosed. Early detection, accurate discrimination between unilateral and bilateral PA and appropriate treatment are vital to prevent cardiovascular and renal complications. The current workup however is a cumbersome, multistep process that may not detect dynamic aldosterone variability or nocturnal hypersecretion. Furthermore, subtype determination with adrenal venous sampling is invasive and technically challenging. Novel dynamic 24-hour steroid profiling may represent a major step forward in the diagnostics and subtyping of PA. Aim: To assess 24-hour rhythmicity of aldosterone and metabolites in PA, and to develop a novel dynamic diagnostic tool to discriminate PA from healthy subjects (HS), using tissue microdialysis. Material and methods: Twenty-minute frequency microdialysis fractions were collected over 24 hours from 64 ambulatory patients with PA (26 bilateral, 24 unilateral, 14 with undetermined subtype) using the U-Rhythm sampler followed by multiplex hormone assay liquid chromatography tandem mass spectrometry (LC-MS/MS). Sixteen patients subsequently treated with adrenalectomy for unilateral PA had additional postoperative samplings. Microdialysis samplings from 214 HS were used as controls. We calculated statistical distributions of dynamic biomarkers of abnormality, and developed a machine learning classifier that discriminates PA from HS. Results: Although there were large interindividual variations, PA patients did show a disturbed circadian rhythmicity of aldosterone compared with HS, including some with multiple very high ultradian spikes. Unilateral PA had higher aldosterone spikes and more disturbed rhythmicity compared with bilateral PA. A profound reduction of aldosterone levels and normalisation of rhythmicity was seen post adrenalectomy. Applying a Random Forest classifier in a subgroup of 20 PA patients, 82% specificity and 85% sensibility were achieved to discriminate PA from HS. Our analysis also suggests 18-hydroxycortisol as the most discriminative dynamic biomarker. Conclusions: We demonstrate for the first time circadian and ultradian rhythms of aldosterone in a large cohort of PA compared with HS. Based on our preliminary results, dynamic 24-hour samplings may provide a novel method for diagnosing and subtyping PA, and assess biochemical cure after adrenalectomy. Presentation: Thursday, June 15, 2023

Toward assessment of human voice biomarkers of brain lesions through explainable deep learning

Lesions in the brain resulting from traumatic injuries or strokes can evolve into speech dysfunction in undiagnosed patients. Employing ML-based tools to analyze the prosody or articulatory phonetics of human speech could be advantageous for early screening of undetected brain injuries. Additionally, explaining the model’s decision-making process can support predictions and take appropriate measures to improve patient voice quality. However, traditional ML methods relying on low-level descriptors (LLDs) may sacrifice detailed temporal dynamics and other speech characteristics. Interpreting these descriptors can also be challenging, requiring significant effort to understand feature relationships and suitable ranges. To address these limitations, this research paper introduces xDMFCCs, a method that identifies interpretive discriminatory acoustic biomarkers from a single speech utterance, providing local and global interpretations of deep learning models in speech applications. To validate this approach, it was implemented to interpret a Convolutional Neural Network (CNN) trained on Mel-frequency Cepstral Coefficients (MFCC) for the binary classification task to differentiate between patients from control vocalizations. The ConvNet achieved promising results with a 75% f-score (75% recall, 76% precision), comparable to conventional machine learning baselines. What sets xDMFCCs apart is its explanation through a 2D time–frequency representation that preserves the complete speech signal. This representation offers a more transparent explanation for differentiating between patients and healthy controls, enhancing interpretability. This advancement enables more detailed and compelling studies in speech acoustic traits of brain lesions. Furthermore, the findings have significant implications for developing low-cost and rapid diagnostics of unnoticed brain lesions.

Integrative metabolome and lipidome analyses of plasma in neovascular macular degeneration

Age-related macular degeneration (AMD) causes irreversible vision-loss among the elderly in industrial countries. Neovascular AMD (nAMD), which refers to late-stage AMD, is characterized by severe vision-threatening choroidal neovascularization (CNV). Herein, we constructed a global metabolic network of nAMD, based on untargeted metabolomic and lipidomic analysis of plasma samples collected from sixty subjects (30 nAMD patients and 30 age-matched controls). Among the nAMD and control groups, 62 and 44 significantly different metabolites were detected in the positive and negative ion modes, respectively. Grouping analysis further showed that lipid and lipid-like molecule-based superclasses contained the highest number of significantly different metabolites. Lipidomic analysis revealed that 53 lipids among the nAMD and control groups differed significantly; these belonged to four major lipid categories (glycerophospholipids, sphingolipids, glycerolipids, and fatty acids). A discriminative biomarker panel comprising 16 metabolites and lipids, which was constructed using multivariate statistical machine learning methods, could effectively identify nAMD cases. Among these 16 compounds, eight were lipids that belonged to three lipid categories (glycerophospholipids, sphingolipids, and prenol lipids). The top three biomarkers with the highest importance scores were all lipids (a glycerophospholipid and two sphingolipids), highlighting the crucial role played by glycerophospholipid and sphingolipid pathways in nAMD. These differences between the metabolic and lipid profiles of nAMD patients and elderly individuals without AMD provide a readout of the overall metabolic status of nAMD. Further insights into the identified discriminative biomarkers may pave the way for future diagnostic and therapeutic interventions for nAMD.

Analysis of the dynamic changes in gut microbiota in patients with different severity in sepsis

BackgroundThe gastrointestinal tract contains a massive microbiota, and targeting the gut could be a potential intervention for sepsis. However, the interaction between sepsis and the intestinal microbiota is defined as an “incompletely understood bidirectional relationship”.MethodsThis retrospective observational cohort study investigated the fecal microbiota of sepsis patients admitted to the Department of Critical Care Medicine of the Central Hospital of Wuhan, China, from May 2019 to January 2020. 14 septic patients were divided into the non-severe group and the severe group according to the Acute Physiology and Chronic Health Evaluation II (APACHE II) score. Herein, fecal samples were serially collected on admission, the third, fourth, and fifth days, and ICU discharge. The fecal microbiota was analyzed by 16S rRNA gene sequencing and its correlation with clinical parameters was evaluated.ResultsBacteroidetes, Firmicutes, and Proteobacteria were dominant phyla at ICU admission, and fecal biodiversity was not significantly different between the non-severe group (APACHE II < 15) and the severe group (APACHE II > 15). However, the diversity of the gut microbiota was significantly lower at ICU discharge than that at ICU admission with the extension of treatment time. Further significant difference flora analysis (LEfSe) showed that the genera Veillonella and Ruminococcus were the most discriminant biomarkers at ICU admission in non-severe and severe patients, respectively, while Enterococcus was the most discriminant biomarker at ICU discharge in all septic patients. Of note, liver function tests, including ALT, AST, TBIL, and DBIL correlated with the prevalence of various bacterial genera.ConclusionsThe diversity of the gut microbiota in patients with sepsis decreases dramatically during ICU stay, and there are distinct dynamic changes in gut microbiota among patients with different severity in sepsis.

Ultrasensitive assay of 8-hydroxy-2′-deoxyguanosine in whole blood using carbon nanotubes based stochastic microsensors

8-hydroxy-2′-deoxyguanosine assay is having an important role in carcinogenesis, and accordingly is recognized as a discriminatory biomarker for early detection of breast cancer. Therefore, this article proposed two stochastic microsensors based on single (SWCNT) and multi (MWCNT) wall carbon nanotubes pastes, modified with α-cyclodextrin (α-CD) for the assay of 8-hydroxy-2′-deoxyguanosine in whole blood. The wider linear concentration range (1 × 10−12–1 × 10−6 mg L−1) was recorded when the microsensor based on SWCNT was used, while the highest sensitivity (7.38 × 107 s−1 g−1 mL) was recorded when the microsensor based on MWCNT was used. The type of carbon nanotube (SWCNT/MWCNT) was not influencing the limit of determination of 8-hydroxy-2′-deoxyguanosine (1 × 10−12 mg L−1). Very good correlations were obtained between the stochastic method and ELISA (the standard method), when used for the assay of 8-hydroxy-2′-deoxyguanosine in whole blood samples.

Platelet indices as a predictor in the differentiation of Behçet's disease from recurrent aphthous stomatitis.

The aim of this retrospective cohort study was to investigate complete blood count parameters in patients with Behçet's disease (BD) who present with oral ulcers and patients with recurrent aphthous stomatitis (RAS) in order to determine whether they could be used as discriminatory biomarkers. This study was conducted between January 2019 and January 2023. The study population consisted of three groups: patients with BD who had oral ulcer manifestation (n=85, BD-Group), patients with idiopathic RAS (n=186, RAS-Group) and healthy controls (n=90, HC-Group). All data about participants, on their first application, including sociodemographic and clinical data, comorbidity status, laboratory results were collected retrospectively from the hospital computer records and patients' charts. The groups were similar in terms of age (p=0.235) and sex distribution (p=0.450). Mean platelet volume (MPV) and plateletcrit values of the BD-Group were significantly lower, while platelet distribution width (PDW) was significantly higher, compared to the other two groups (p<0.001 for all). Low MPV (<9.15) (56.47% sensitivity and 90.86% specificity), high PDW (≥15.75) (75.00% sensitivity and 94.96% specificity) and low plateletcrit (<0.237) (55.29% sensitivity and 79.46% specificity) could significantly distinguish BD patients with oral ulcer onset from patients with RAS. PDW, MPV, and plateletcrit may be useful biomarkers in the differential diagnosis of oral ulcers when distinguishing between BD and RAS. However, these results need to be supported by further comprehensive studies.

Olfactory status in neurodegeneration with brain iron accumulation disorders.

Olfactory dysfunction has been suggested as a diagnostic and discriminative biomarker in some neurodegenerative disorders. However, there are few studies regarding the olfactory status in rare diseases including neurodegeneration with brain iron accumulation (NBIA) disorders. Genetically-confirmed NBIA patients were enrolled. Neurological and cognitive examinations were conducted according to the Pantothenate Kinase-Associated Neurodegeneration-Disease Rating Scale (PKAN-DRS) and the Mini-Mental State Examination (MMSE) questionnaire, respectively. Olfaction was assessed in three domains of odor threshold (OT), odor discrimination (OD), odor identification (OI), and total sum (TDI) score by the Sniffin' Sticks test. The olfactory scores were compared to a control group and a normative data set. Thirty-seven patients, including 22 PKAN, 6 Kufor Rakeb syndrome, 4 Mitochondrial membrane Protein-Associated Neurodegeneration (MPAN), 5 cases of other 4 subtypes, and 37 controls were enrolled. The mean PKAN-DRS score was 51.83±24.93. Sixteen patients (55.2%) had normal cognition based on MMSE. NBIA patients had significantly lower olfactory scores compared to the controls in TDI and all three subtests, and 60% of them were hyposmic according to the normative data. Including only the cognitively-normal patients, still, OI and TDI scores were significantly lower compared to the controls. The phospholipase A2-Associated Neurodegeneration (PLAN) and MPAN patients had a significantly lower OI score compared to the cognitively-matched PKAN patients. Olfactory impairment as a common finding in various subtypes of NBIA disorder can potentially be considered a discriminative biomarker. Better OI in PKAN compared to PLAN and MPAN patients may be related to the different underlying pathologies.