scholarly journals Pulmonary Involvement in Adult Patients with Inborn Errors of Metabolism

Respiration ◽  
2017 ◽  
Vol 94 (1) ◽  
pp. 2-13 ◽  
Author(s):  
Christel Tran ◽  
Frederic Barbey ◽  
Romain Lazor ◽  
Luisa Bonafé
Keyword(s):  
Inborn Errors Of Metabolism ◽  
Pulmonary Involvement ◽  
Adult Patients ◽  
Inborn Errors

scholarly journals Lifestyle, Nutritional and Psychological Changes in Adult Patients with Inborn Errors of Metabolism during COVID 19 Pandemic: Resultss from an Online Survey

2021 ◽  
Vol 2 (3) ◽  
pp. 100-103
Author(s):  
Gugelmo G ◽  
Schiff S ◽  
Lovato E ◽  
Lenzini L ◽  
Boscari F ◽  
...  
Keyword(s):  
Physical Activity ◽  
Inborn Errors Of Metabolism ◽  
Online Survey ◽  
Nutritional Therapy ◽  
Health Condition ◽  
Lifestyle Changes ◽  
Adult Patients ◽  
Psychological Status ◽  
Inborn Errors ◽  
Metabolic Crisis

When COVID-19 pandemic out broke in Italy, during the lockdown from March to May 2020, Inborn Errors of Metabolism (IEM) patients were at risk of not getting their dietary special products and routine visits. Moreover, during pandemic, psychological difficulties might have arose in these subjects, even more severe than in the general population due to the worries about acute decompensation caused by a possible COVID-19. In order to evaluate the patients’ perception of the outbreak situation and their related needs, three simple online anonymous surveys drawn up by Google Forms were sent to patients and families referring to our Adult IEM Center. Answers were collected between April and May 2020. Questionnaires investigated nutritional and lifestyle changes and psychological status using validated psychological tools. 19 patients with IEM filled out our survey (Median age 26-30 years). The most common nutritional therapy was low protein diet. During quarantine 12% patients failed to follow their usual medical diet, 65% reduced their physical activity and no one underwent an acute metabolic crisis. 57% of patients asked for more frequent access to the reference center. 33% of patients showed stress perceived of clinical relevance and general health perception were out of normal in 40% of patients. In conclusion, during quarantine some patients reported difficulty in following their medical diet or physical activity and were clinically stressed. Despite this, no one experienced a metabolic crisis, but asked for contacting the Metabolic Team in different ways than usual due to worries about their health condition. Telemedicine, the possibility of clinical follow-up at home patient (Including blood tests) and reservation of non-COVID-19 beds for hospital admission of IEM patients may have contributed to help IEM adult patients in better face this emergency time.

Clinical characteristics of patients with lysosomal diseases in a Spanish cohort of 499 adult patients with inborn errors of metabolism from university hospitals

2017 ◽  
Vol 120 (1-2) ◽  
pp. S106-S107
Author(s):  
Jordi Pérez-López ◽  
Leticia Ceberio-Hualde ◽  
José Salvador García-Morillo ◽  
Josep Maria Grau-Junyent ◽  
Alvaro Hermida-Ameijeiras ◽  
...  
Keyword(s):  
Inborn Errors Of Metabolism ◽  
Clinical Characteristics ◽  
Adult Patients ◽  
University Hospitals ◽  
Inborn Errors ◽  
Lysosomal Diseases ◽  
Spanish Cohort

scholarly journals Clinical characteristics of adult patients with inborn errors of metabolism in Spain: A review of 500 cases from university hospitals

2017 ◽  
Vol 10 ◽  
pp. 92-95 ◽  
Author(s):  
J. Pérez-López ◽  
L. Ceberio-Hualde ◽  
J.S. García-Morillo ◽  
J.M. Grau-Junyent ◽  
A. Hermida Ameijeiras ◽  
...  
Keyword(s):  
Inborn Errors Of Metabolism ◽  
Clinical Characteristics ◽  
Adult Patients ◽  
University Hospitals ◽  
Inborn Errors

scholarly journals Using Out-of-Batch Reference Populations to Improve Untargeted Metabolomics for Screening Inborn Errors of Metabolism

Metabolites ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 8
Author(s):  
Michiel Bongaerts ◽  
Ramon Bonte ◽  
Serwet Demirdas ◽  
Edwin H. Jacobs ◽  
Esmee Oussoren ◽  
...  
Keyword(s):  
Regression Model ◽  
Inborn Errors Of Metabolism ◽  
Untargeted Metabolomics ◽  
Detection Accuracy ◽  
Inborn Errors ◽  
Mixed Effect ◽  
Metabolomics Data ◽  
Normalization Methods ◽  
Metabolite Concentrations ◽  
Reference Samples

Untargeted metabolomics is an emerging technology in the laboratory diagnosis of inborn errors of metabolism (IEM). Analysis of a large number of reference samples is crucial for correcting variations in metabolite concentrations that result from factors, such as diet, age, and gender in order to judge whether metabolite levels are abnormal. However, a large number of reference samples requires the use of out-of-batch samples, which is hampered by the semi-quantitative nature of untargeted metabolomics data, i.e., technical variations between batches. Methods to merge and accurately normalize data from multiple batches are urgently needed. Based on six metrics, we compared the existing normalization methods on their ability to reduce the batch effects from nine independently processed batches. Many of those showed marginal performances, which motivated us to develop Metchalizer, a normalization method that uses 10 stable isotope-labeled internal standards and a mixed effect model. In addition, we propose a regression model with age and sex as covariates fitted on reference samples that were obtained from all nine batches. Metchalizer applied on log-transformed data showed the most promising performance on batch effect removal, as well as in the detection of 195 known biomarkers across 49 IEM patient samples and performed at least similar to an approach utilizing 15 within-batch reference samples. Furthermore, our regression model indicates that 6.5–37% of the considered features showed significant age-dependent variations. Our comprehensive comparison of normalization methods showed that our Log-Metchalizer approach enables the use out-of-batch reference samples to establish clinically-relevant reference values for metabolite concentrations. These findings open the possibilities to use large scale out-of-batch reference samples in a clinical setting, increasing the throughput and detection accuracy.

Sign in / Sign up

Export Citation Format

Share Document