Urinary Urea, Uric Acid and Hippuric Acid as Potential Biomarkers in Multiple Sclerosis Patients

Atya, Hanaa B.; Ali, Sahar A.; Hegazy, Mohamed I.; Sharkawi, Fathia Zaky El

doi:10.1007/s12291-017-0661-6

Cited by 11 publications

(11 citation statements)

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“…Indole metabolites can prevent stress‐induced injury in the gastrointestinal tract, enhancing the barrier properties of epithelial cells, regulating the expression of pro‐inflammatory pathways (e.g., IL‐8), and enhancing anti‐inflammatory pathways (e.g., IL‐10) 36,37 . Hippuric acid production is dependent on intestinal bacteria, and reduced urinary level of hippuric acid due to gut microbiota alterations has been identified as the cause of inflammatory conditions such as Crohn's disease and multiple sclerosis 38,39 . Hydroxyphenyllactic acid has been identified as an antioxidant compound with antifungal capability 40 .…”

Section: Discussionmentioning

confidence: 99%

“…36,37 Hippuric acid production is dependent on intestinal bacteria, and reduced urinary level of hippuric acid due to gut microbiota alterations has been identified as the cause of inflammatory conditions such as Crohn's disease and multiple sclerosis. 38,39 Hydroxyphenyllactic acid has been identified as an antioxidant compound with antifungal capability. 40 In this study, five differential metabolites (Ortho hydroxyphenylacetic acid, hydroxyphenyllactic acid, hippuric acid, indole-3-carboxylic acid, and 3-indole-propionic acid) were found to be involved in the metabolic pathway of aromatic amino acids.…”

Section: (A) (B)mentioning

confidence: 99%

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Metabolic alterations in patients with Helicobacter pylori‐related gastritis: The H. pylori‐gut microbiota‐metabolism axis in progression of the chronic inflammation in the gastric mucosa

et al. 2023

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Epidemiological studies have recently revealed that the global prevalence of Helicobacter pylori infection varies between 30% and 50% in developed countries and between 85% and 95% in developing countries. 1,2 H. pylori is a Gram-negative, microaerophilic bacteria that infects humans through oral-oral or fecal-oral transmission. Once inside the body, it adapts well to the acidic environment of the gastric epithelium, and most often, it induces an inflammatory response in the mucosa. 3 The presence of H. pylori has been associated with peptic ulcers and gastritis; if left untreated, H. pylori-induced gastritis can become chronic. The majority of H. pylori-positive subjects

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Section: Discussionmentioning

confidence: 99%

Section: (A) (B)mentioning

confidence: 99%

Metabolic alterations in patients with Helicobacter pylori‐related gastritis: The H. pylori‐gut microbiota‐metabolism axis in progression of the chronic inflammation in the gastric mucosa

et al. 2023

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“…Thus, future research may offer new perspectives. Hippuric acid, a combination of benzoic acid and glycine, is the main metabolite in the body and is a normal component of urine [ 31 ]. It is the most commonly used biomarker for biomonitoring occupational toluene exposure [ 32 ].…”

Section: Discussionmentioning

confidence: 99%

Correlation analysis of aqueous humor metabolomics with myopic axial length and choroidal parameters

et al. 2023

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Background To explore differential metabolites in the aqueous humor of patients with different axial lengths and their correlations with axial length and choroidal parameters. Methods In this study, we included 12 patients with axial lengths less than 24 mm, 11 patients with axial lengths between 24 and 26 mm, and 11 patients with axial lengths greater than 26 mm. We collected their aqueous humor samples during cataract surgery for liquid chromatography-mass spectrometry metabolomic analysis. Simultaneously, we collected relevant clinical parameters such as axial length, subfoveal choroidal thickness, and choroidal vascular index. Correlations between clinical data, differential metabolites, and clinical indicators were analyzed. In addition, we plotted receiver operating characteristic curves. Results The results showed that axial length was significantly negatively correlated with choroidal thickness (r=-0.7446, P < 0.0001), and that several differential metabolites were significantly correlated with certain clinical parameters. After analyzing receiver operating characteristic curves, 5-methoxytryptophol and cerulenin were found to have excellent discriminative power, demonstrating their potential as biomarkers. In the enrichment analysis, we found that the differential metabolites among each group were involved in several special pathways (Taurine and Hypotaurine Metabolism, Vitamin B6 Metabolism, Pantothenate, and coenzyme A Biosynthesis), suggesting that abnormalities in these metabolic pathways may play a role in the process of axial myopia. Conclusions Our study identified alterations in certain metabolic pathways in different axial lengths. At the same time, we found several metabolites with significant correlation with clinical indicators, among which 5-methoxytryptophol and cerulenin were associated with axial myopia. Clinical trial registration Registration date:11/04/2022. Trial registration number: ChiCTR2200058575. Trial registry: The First Affiliated Hospital of the Zhejiang University School of Medicine.

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“…38 In the P4 models, which excluded the laboratory covariates not routinely collected in clinical practice, we observed other wellknown markers such as age of onset of disease, time since first symptom, volumes of T2 lesions, and T1 hypointense lesions become more predictive 39,40 in the absence of the laboratory covariates that have not been studied in the MS clinical literature. The role of laboratory covariates such as urate, a predictor of disease activity in our P3-T-12 model (Appendix S1: Figure S1), in MS disease has been previously investigated, [41][42][43][44][45] but its precise understanding is currently lacking. Further investigations should focus on the assessment of the prognostic (treatment independent) versus predictive (treatment dependent) nature of these factors.…”

Section: Discussionmentioning

confidence: 99%

Predicting disease activity in patients with multiple sclerosis: An explainable machine‐learning approach in the Mavenclad trials

Basu

Munafo

Ben-Amor

et al. 2022

CPT Pharmacom & Syst Pharma

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Multiple sclerosis (MS) is among the most common autoimmune disabling neurological conditions of young adults and affects more than 2.3 million people worldwide. Predicting future disease activity in patients with MS based on their pathophysiology and current treatment is pivotal to orientate future treatment. In this respect, we used machine learning to predict disease activity status in patients with MS and identify the most predictive covariates of this activity. The analysis is conducted on a pooled population of 1935 patients enrolled in three cladribine tablets clinical trials with different outcomes: relapsing–remitting MS (from CLARITY and CLARITY‐Extension trials) and patients experiencing a first demyelinating event (from the ORACLE‐MS trial). We applied gradient‐boosting (from XgBoost library) and Shapley Additive Explanations (SHAP) methods to identify patients' covariates that predict disease activity 3 and 6 months before their clinical observation, including patient baseline characteristics, longitudinal magnetic resonance imaging readouts, and neurological and laboratory measures. The most predictive covariates for early identification of disease activity in patients were found to be treatment duration, higher number of new combined unique active lesion count, higher number of new T1 hypointense black holes, and higher age‐related MS severity score. The outcome of this analysis improves our understanding of the mechanism of onset of disease activity in patients with MS by allowing their early identification in clinical settings and prompting preventive measures, therapeutic interventions, or more frequent patient monitoring.

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Urinary Urea, Uric Acid and Hippuric Acid as Potential Biomarkers in Multiple Sclerosis Patients

Cited by 11 publications

References 50 publications

Metabolic alterations in patients with Helicobacter pylori‐related gastritis: The H. pylori‐gut microbiota‐metabolism axis in progression of the chronic inflammation in the gastric mucosa

Metabolic alterations in patients with Helicobacter pylori‐related gastritis: The H. pylori‐gut microbiota‐metabolism axis in progression of the chronic inflammation in the gastric mucosa

Correlation analysis of aqueous humor metabolomics with myopic axial length and choroidal parameters

Predicting disease activity in patients with multiple sclerosis: An explainable machine‐learning approach in the Mavenclad trials

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