Several relatively recently published studies have shown changes in plasma metabolites in various viral diseases such as Zika, Dengue, RSV or SARS-CoV-1. The aim of this study was to analyze the metabolome profile of patients during acute COVID-19 approximately one month after the acute infection and to compare these results with healthy (SARS-CoV-2-negative) controls. The metabolome analysis was performed by NMR spectroscopy from the peripheral blood of patients and controls. The blood samples were collected on 3 different occasions (at admission, during hospitalization and on control visit after discharge from the hospital). When comparing sample groups (based on the date of acquisition) to controls, there is an indicative shift in metabolomics features based on the time passed after the first sample was taken towards controls. Based on the random forest algorithm, there is a strong discriminatory predictive value between controls and different sample groups (AUC equals 1 for controls versus samples taken at admission, Mathew correlation coefficient equals 1). Significant metabolomic changes persist in patients more than a month after acute SARS-CoV-2 infection. The random forest algorithm shows very strong discrimination (almost ideal) when comparing metabolite levels of patients in two various stages of disease and during the recovery period compared to SARS-CoV-2-negative controls.

Metabolomics is a relatively new research area that focuses mostly on the profiling of selected molecules and metabolites within the organism. A SARS-CoV-2 infection itself can lead to major disturbances in the metabolite profile of the infected individuals. The aim of this study was to analyze metabolomic changes in the urine of patients during the acute phase of COVID-19 and approximately one month after infection in the recovery period. We discuss the observed changes in relation to the alterations resulting from changes in the blood plasma metabolome, as described in our previous study. The metabolome analysis was performed using NMR spectroscopy from the urine of patients and controls. The urine samples were collected at three timepoints, namely upon hospital admission, during hospitalization, and after discharge from the hospital. The acute COVID-19 phase induced massive alterations in the metabolic composition of urine was linked with various changes taking place in the organism. Discriminatory analyses showed the feasibility of successful discrimination of COVID-19 patients from healthy controls based on urinary metabolite levels, with the highest significance assigned to citrate, Hippurate, and pyruvate. Our results show that the metabolomic changes persist one month after the acute phase and that the organism is not fully recovered.

Rationale: Severe alcohol-associated hepatitis (SAH) is the most critical, acute, inflammatory phenotype within the alcohol-associated liver disease (ALD) spectrum, characterized by high 30- and 90-day mortality. Since several decades, corticosteroids (CS) are the only approved pharmacotherapy offering highly limited survival benefits. Contextually, there is an evident demand for 3PM innovation in the area meeting patients’ needs and improving individual outcomes. Fecal microbiota transplantation (FMT) has emerged as one of the new potential therapeutic options. In this study, we aimed to address the crucial 3PM domains in order to assess (i) the impact of FMT on mortality in SAH patients beyond CS, (ii) to identify factors associated with the outcome to be improved (iii) the prediction of futility, (iv) prevention of suboptimal individual outcomes linked to increased mortality, and (v) personalized allocation of therapy. Methods: We conducted a prospective study (NCT04758806) in adult patients with SAH who were non-responders (NR) to or non-eligible (NE) for CS between January 2018 and August 2022. The intervention consisted of five 100 ml of FMT, prepared from 30 g stool from an unrelated healthy donor and frozen at − 80 °C, administered daily to the upper gastrointestinal (GI) tract. We evaluated the impact of FMT on 30- and 90-day mortality which we compared to the control group selected by the propensity score matching and treated by the standard of care; the control group was derived from the RH7 registry of patients hospitalized at the liver unit (NCT04767945). We have also scrutinized the FMT outcome against established and potential prognostic factors for SAH — such as the model for end-stage liver disease (MELD), Maddrey Discriminant Function (MDF), acute-on-chronic liver failure (ACLF), Liver Frailty Index (LFI), hepatic venous-portal pressure gradient (HVPG) and Alcoholic Hepatitis Histologic Score (AHHS) — to see if the 3PM method assigns them a new dimension in predicting response to therapy, prevention of suboptimal individual outcomes, and personalized patient management. Results: We enrolled 44 patients with SAH (NR or NE) on an intention-to-treat basis; we analyzed 33 patients per protocol for associated factors (after an additional 11 being excluded for receiving less than 5 doses of FMT), and 31 patients by propensity score matching for corresponding individual outcomes, respectively. The mean age was 49.6 years, 11 patients (33.3%) were females. The median MELD score was 29, and ACLF of any degree had 27 patients (81.8%). FMT improved 30-day mortality (p = 0.0204) and non-significantly improved 90-day mortality (p = 0.4386). Univariate analysis identified MELD ≥ 30, MDF ≥ 90, and ACLF grade > 1 as significant predictors of 30-day mortality, (p = 0.031; p = 0.014; p = 0.034). Survival was not associated with baseline LFI, HVPG, or AHHS. Conclusions and recommendations in the framework of 3PM: In the most difficult-to-treat sub-cohort of patients with SAH (i.e., NR/NE), FMT improved 30-day mortality. Factors associated with benefit included MELD ≤ 30, MDF ≤ 90, and ACLF < 2. These results support the potential of gut microbiome as a therapeutic target in the context of 3PM research and vice versa — to use 3PM methodology as the expedient unifying template for microbiome research. The results allow for immediate impact on the innovative concepts of (i) personalized phenotyping and stratification of the disease for the clinical research and practice, (ii) multilevel predictive diagnosis related to personalized/precise treatment allocation including evidence-based (ii) prevention of futile and sub-optimally effective therapy, as well as (iii) targeted prevention of poor individual outcomes in patients with SAH. Moreover, our results add to the existing evidence with the potential to generate new research along the SAH’s pathogenetic pathways such as diverse individual susceptibility to alcohol toxicity, host-specific mitochondrial function and systemic inflammation, and the role of gut dysbiosis thereof.

Background and aim: COVID-19 can be presented with various gastrointestinal symptoms. Shortly after the pandemic outbreak, several machine learning algorithms were implemented to assess new diagnostic and therapeutic methods for this disease. The aim of this study is to assess gastrointestinal and liver-related predictive factors for SARS-CoV-2 associated risk of hospitalization. Methods: Data collection was based on a questionnaire from the COVID-19 outpatient test center and from the emergency department at the University Hospital in combination with the data from internal hospital information system and from a mobile application used for telemedicine follow-up of patients. For statistical analysis SARS-CoV-2 negative patients were considered as controls in three different SARS-CoV-2 positive patient groups (divided based on severity of the disease). The data were visualized and analyzed in R version 4.0.5. The Chi-squared or Fisher test was applied to test the null hypothesis of independence between the factors followed, where appropriate, by the multiple comparisons with the Benjamini Hochberg adjustment. The null hypothesis of the equality of the population medians of a continuous variable was tested by the Kruskal Wallis test, followed by the Dunn multiple comparisons test. In order to assess predictive power of the gastrointestinal parameters and other measured variables for predicting an outcome of the patient group the Random Forest machine learning algorithm was trained on the data. The predictive ability was quantified by the ROC curve, constructed from the Out-of-Bag data. Matthews correlation coefficient was used as a one-number summary of the quality of binary classification. The importance of the predictors was measured using the Variable Importance. A 2D representation of the data was obtained by means of Principal Component Analysis for mixed type of data. Findings with the p-value below 0.05 were considered statistically significant. Results: A total of 710 patients were enrolled in the study. The presence of diarrhea and nausea was significantly higher in the emergency department group than in the COVID-19 outpatient test center. Among liver enzymes only aspartate transaminase (AST) has been significantly elevated in the hospitalized group compared to patients discharged home. Based on the Random Forest algorithm, AST has been identified as the most important predictor followed by age or diabetes mellitus. Diarrhea and bloating have also predictive importance, although much lower than AST. Conclusion: SARS-CoV-2 positivity is connected with isolated AST elevation and the level is linked with the severity of the disease. Furthermore, using the machine learning Random Forest algorithm, we have identified the elevated AST as the most important predictor for COVID-19 related hospitalizations.

Introduction: There is a substantial lack of data regarding the prevalence of irritable bowel syndrome (IBS) and functional dyspepsia (FD) in the region of Central/Eastern Europe. It is a well-described and known fact that environmental, ethnic, dietary, and cultural factors can influence the reporting of symptoms. Therefore, we aim to provide the first data documenting the prevalence of specific disorders of gut-brain interaction in Slovakia. Methods: This is a multicenter-based study. The study population consists of medical students from three medical faculties in Slovakia, mainly with Slovakian and Scandinavian permanent residency. Data collection was performed by means of anonymous questionnaires consisting of several demographic questions. Two forms of questionnaires were used. One was in paper form, and the second was distributed via email. Results: Altogether, 1061 students participated in this study. Symptoms of IBS were presented in 7.3% of students, and FD in 13%. In the Slovakian group, these were FD 12%, and IBS 7%. The subgroup from Scandinavia shows a prevalence of IBS of 11.7% and FD of 14.0%. A lack of exercise and a vegan diet are related to a higher presence of FD. Conclusion: The results of this multicentre study represent the first published data for the presence of symptoms of IBS and FD in Slovakia. Our data also show a significantly higher prevalence of IBS in students from Scandinavia compared with those from Central/Eastern Europe. A higher frequency of physical exercise is associated with a lower presence of symptoms of FD. On the other hand, the symptoms of FD were mostly prevalent in the group adhering to a vegan and vegetarian type diet.

INTRODUCTION: The diagnostic Rome criteria, classify disorders of gut-brain interaction (DGBI) according to the anatomical region of the gastrointestinal (GI) tract from which the predominant symptom are perceived to originate. A study in Asian DGBI patients using an Enhanced Asian Rome questionnaire (EAR3Q) identified symptom clusters involving multiple anatomical section. Our aim was to investigate if DGBI symptom groupings involving more than 1 anatomical region can be found in both Eastern and Western DGBI patients. METHODS: Physician-diagnosed DGBI patients, recruited from 5 Eastern and 6 Western, completed the EAR4Q. We performed an exploratory and confirmatory factor analysis on questionnaire data to identify symptom groupings, describing distinct DGBI diagnostic entities. RESULTS: In total, 1,074 DGBI patients (66.8% women, 42.6 ± 14.6 years) were recruited. We identified 10 distinct symptom groupings: (i) irritable bowel syndrome predominant diarrhea triggered by a meal and relieved by bowel movement or passing of flatus; (ii) constipation; (iii) upper GI symptoms with predominant regurgitation, nausea and vomiting; (iv) upper and lower GI symptoms relieved by bowel movement or flatus; (v) epigastric pain or burning related to bowel movement or passing flatus; (vi) lower GI symptoms triggered or worsened by bowel movement or passing flatus; (vii) meal-related pain and gas symptoms; (viii) globus and dysphagia; (ix) functional chest pain and heartburn; (x) constipation and belching relieved by bowel movement or passing flatus. Although some symptom groupings aligned with the Rome IV diagnostics, several clearly include multiple anatomical regions, surpassing the scope of the Rome regional subdivision approach. CONCLUSION: Our study revealed 10 distinct symptom clusters. Some aligned with Rome criteria but other extended beyond its strict anatomical divisions, challenging a key assumption of the Rome classification framework. These results provide novel insight on a multicontinental scale about the complexity of symptom patterns in DGBI and their anatomical relationships.