Clinical and biochemical footprints of inherited metabolic diseases. IX. Metabolic ear disease

Damages to the ear are very diverse and can depend on the type of inherited metabolic diseases (IMD). Indeed, IMDs can affect all parts of the auditory system, from the outer ear to the central auditory process. We have identified 219 IMDs associated with various types of ear involvement which we classified into five groups according to the lesion site of the auditory system: congenital external ear abnormalities, acquired external ear abnormalities, middle ear involvement, inner ear or retrocochlear involvement, and unspecified hearing loss. This represents the ninth issue in a series of educational summaries providing a comprehensive and updated list of metabolic differential diagnoses according to system involvement. Keywords: Conductive hearing loss; Ear; External ear; Hearing loss; Inborn errors of metabolism; Inherited metabolic diseases; Sensorineural hearing loss; Tinnitus

The Relevancy of Data Regarding the Metabolism of Iron to Our Understanding of Deregulated Mechanisms in ALS; Hypotheses and Pitfalls

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease caused by the loss of motor neurons. Its etiology remains unknown, but several pathophysiological mechanisms are beginning to explain motor neuronal death, as well as oxidative stress. Iron accumulation has been observed in both sporadic and familial forms of ALS, including mouse models. Therefore, the dysregulation of iron metabolism could play a role in the pathological oxidative stress in ALS. Several studies have been undertaken to describe iron-related metabolic markers, in most cases focusing on metabolites in the bloodstream due to few available data in the central nervous system. Reports of accumulation of iron, high serum ferritin, and low serum transferrin levels in ALS patients have encouraged researchers to consider dysregulated iron metabolism as an integral part of ALS pathophysiology. However, it appears complicated to suggest a general mechanism due to the diversity of models and iron markers studied, including the lack of consensus among all of the studies. Regarding clinical study reports, most of them do not take into account confusion biases such as inflammation, renal dysfunction, and nutritional status. Furthermore, the iron regulatory pathways, particularly involving hepcidin, have not been thoroughly explored yet within the pathogenesis of iron overload in ALS. In this sense, it is also essential to explore the relation between iron overload and other ALS-related events, such as neuro-inflammation, protein aggregation, and iron-driven cell death, termed ferroptosis. In this review, we point out limits of the designs of certain studies that may prevent the understanding of the role of iron in ALS and discuss the relevance of the published data regarding the pathogenic impact of iron metabolism deregulation in this disease and the therapeutics targeting this pathway

Metabolomics Biomarkers: A Strategy Toward Therapeutics Improvement in ALS

Biomarkers research in amyotrophic lateral sclerosis (ALS) holds the promise of improving ALS diagnosis, follow-up of patients, and clinical trials outcomes. Metabolomics have a big impact on biomarkers identification. In this mini-review, we provide the main findings of metabolomics studies in ALS and discuss the most relevant therapeutics attempts that targeted some prominent alterations found in ALS, like glutamate excitotoxicity, oxidative stress, alterations in energetic metabolism, and creatinine levels. Metabolomics studies have reported putative diagnosis or prognosis biomarkers, but discrepancies among these studies did not allow validation of metabolic biomarkers for clinical use in ALS. In this context, we wonder whether metabolomics knowledge could improve ALS therapeutics. As metabolomics identify specific metabolic pathways modified by disease progression and/or treatment, we support that adjuvant or combined treatment should be used to rescue these pathways, creating a new perspective for ALS treatment. Some ongoing clinical trials are already trying to target these pathways. As clinical trials in ALS have been disappointing and considering the heterogeneity of the disease presentation, we support the application of a pharmacometabolomic approach to evaluate the individual response to drug treatments and their side effects, enabling the development of personalized treatments for ALS. We suggest that the best strategy to apply metabolomics for ALS therapeutics progress is to establish a metabolic signature for ALS patients in order to improve the knowledge of patient metabotypes, to choose the most adequate pharmacological treatment, and to follow the drug response and side effects, based on metabolomics biomarkers

1H-13C NMR-based urine metabolic profiling in autism spectrum disorders.

International audienceAutism Spectrum Disorders (ASD) are a group of developmental disorders caused by environmental and genetic factors. Diagnosis is based on behavioral and developmental signs detected before 3 years of age with no reliable biological marker. The purpose of this study was to evaluate the potential use of a 2D NMR-based approach to express the global biochemical signature of autistic individuals compared to normal controls. This technique has greater spectral resolution than to 1D (1)H NMR spectroscopy, which is limited by overlapping signals. The urinary metabolic profiles of 30 autistic and 28 matched healthy children were obtained using a (1)H-(13)C NMR-based approach. The data acquired were processed by multivariate orthogonal partial least-squares discriminant analysis (OPLS-DA). Some discriminating metabolites were identified: β-alanine, glycine, taurine and succinate concentrations were significatively higher, and creatine and 3-methylhistidine concentrations were lower in autistic children than in controls. We also noted differences in several other metabolites that were unidentified but characterized by a cross peak correlation in (1)H-(13)C HSQC. Statistical models of (1)H and (1)H-(13)C analyses were compared and only 2D spectra allowed the characterization of statistically relevant changes [R(2)Y(cum)=0.78 and Q(2)(cum)=0.60] in the low abundance metabolites. This method has the potential to contribute to the diagnosis of neurodevelopment disorders but needs to be validated on larger cohorts and on other developmental disorders to define its specificity

Early-onset ventilator-associated pneumonia in adults randomized clinical trial: comparison of 8 versus 15 days of antibiotic treatment

International audiencePurposeThe optimal treatment duration for ventilator-associated pneumonia is based on one study dealing with late-onset of the condition. Shortening the length of antibiotic treatment remains a major prevention factor for the emergence of multiresistant bacteria.ObjectiveTo demonstrate that 2 different antibiotic treatment durations (8 versus 15 days) are equivalent in terms of clinical cure for early-onset ventilator-associated pneumonia.MethodsRandomized, prospective, open, multicenter trial carried out from 1998 to 2002.MeasurementsThe primary endpoint was the clinical cure rate at day 21. The mortality rate was evaluated on days 21 and 90.Results225 patients were included in 13 centers. 191 (84.9%) patients were cured: 92 out of 109 (84.4%) in the 15 day cohort and 99 out of 116 (85.3%) in the 8 day cohort (difference = 0.9%, odds ratio = 0.929). 95% two-sided confidence intervals for difference and odds ratio were [−8.4% to 10.3%] and [0.448 to 1.928] respectively. Taking into account the limits of equivalence (10% for difference and 2.25 for odds ratio), the objective of demonstrative equivalence between the 2 treatment durations was fulfilled. Although the rate of secondary infection was greater in the 8 day than the 15 day cohort, the number of days of antibiotic treatment remained lower in the 8 day cohort. There was no difference in mortality rate between the 2 groups on days 21 and 90.ConclusionOur results suggest that an 8-day course of antibiotic therapy is safe for early-onset ventilator-associated pneumonia in intubated patients

GC-MS-based urine metabolic profiling of autism spectrum disorders.: GC-MS-based Urine Metabolic Profiling in ASD

International audienceAutism spectrum disorders (ASD) are a group of neurodevelopmental disorders resulting from multiple factors. Diagnosis is based on behavioural and developmental signs detected before 3 years of age, and there is no reliable biological marker. The purpose of this study was to evaluate the value of gas chromatography combined with mass spectroscopy (GC-MS) associated with multivariate statistical modeling to capture the global biochemical signature of autistic individuals. GC-MS urinary metabolic profiles of 26 autistic and 24 healthy children were obtained by liq/liq extraction, and were or were not subjected to an oximation step, and then were subjected to a persilylation step. These metabolic profiles were then processed by multivariate analysis, in particular orthogonal partial least-squares discriminant analysis (OPLS-DA, R(2)Y(cum) = 0.97, Q(2)(cum) = 0.88). Discriminating metabolites were identified. The relative concentrations of the succinate and glycolate were higher for autistic than healthy children, whereas those of hippurate, 3-hydroxyphenylacetate, vanillylhydracrylate, 3-hydroxyhippurate, 4-hydroxyphenyl-2-hydroxyacetate, 1H-indole-3-acetate, phosphate, palmitate, stearate, and 3-methyladipate were lower. Eight other metabolites, which were not identified but characterized by a retention time plus a quantifier and its qualifier ion masses, were found to differ between the two groups. Comparison of statistical models leads to the conclusion that the combination of data obtained from both derivatization techniques leads to the model best discriminating between autistic and healthy groups of children

Metabolomics Study of Urine in Autism Spectrum Disorders Using a Multiplatform Analytical Methodology

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder with no clinical biomarker. Aims of this study were to characterize a metabolic signature of ASD, and to evaluate multi-platform analytical methodologies in order to develop predictive tools for diagnosis and disease follow up. Urines were analyzed using: 1H- and 1 H-13C-NMR-based approaches and LC-HRMS-based approaches (ESI+ and ESI- on a HILIC and C18 chromatography column). Data tables obtained from the six analytical modalities on a training set of 46 urines (22 autistic children and 24 controls) were processed by multivariate analysis (OPLS-DA). Prediction of each of these OPLS-DA models were then evaluated using a prediction set of 16 samples (8 autistic children and 8 controls) and ROC curves. Thereafter, a data fusion block-scaling OPLS-DA model was generated from the 6 best models obtained for each modality. This fused OPLSDA model showed an enhanced performance (R 2Y(cum)=0.88, Q 2 (cum)=0.75) compared to each analytical modality model, as well as a better predictive capacity (AUC=0.91, p-value 0.006). Metabolites that are most significantly different between autistic and control children (p<0.05) are indoxyl sulfate, N-\u2329-Acetyl-L-arginine, methyl guanidine and phenylacetylglutamine. This multi-modality approach has the potential to contribute to find robust biomarkers and characterize a metabolic phenotype of the ASD population