scholarly journals Global birth prevalence and mortality from inborn errors of metabolism: a systematic analysis of the evidence

2018 ◽  
Vol 8 (2) ◽  
Author(s):  
Donald Waters ◽  
Davies Adeloye ◽  
Daisy Woolham ◽  
Elizabeth Wastnedge ◽  
Smruti Patel ◽  
...  
Keyword(s):  
Inborn Errors Of Metabolism ◽  
Inborn Errors ◽  
Systematic Analysis ◽  
Birth Prevalence

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.

scholarly journals Spectrum analysis of inborn errors of metabolism for expanded newborn screening in a northwestern Chinese population

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ruixue Zhang ◽  
Rong Qiang ◽  
Chengrong Song ◽  
Xiaoping Ma ◽  
Yan Zhang ◽  
...  
Keyword(s):  
Newborn Screening ◽  
Inborn Errors Of Metabolism ◽  
Northwest China ◽  
Methylmalonic Acidemia ◽  
Shaanxi Province ◽  
Inborn Errors ◽  
Organic Acidurias ◽  
Genetic Characteristics ◽  
Disease Spectrum ◽  
Expanded Newborn Screening

AbstractExpanded newborn screening facilitates early identification and intervention of patients with inborn errors of metabolism (IEMs), There is a lack of disease spectrum data for many areas in China. To determine the disease spectrum and genetic characteristics of IEMs in Xi'an city of Shaanxi province in northwest China, 146152 newborns were screening by MSMS from January 2014 to December 2019 and 61 patients were referred to genetic analysis by next generation sequencing (NGS) and validated by Sanger sequencing. Seventy-five newborns and two mothers were diagnosed with IEMs, with an overall incidence of 1:1898 (1:1949 without mothers). There were 35 newborns with amino acidemias (45.45%, 1:4176), 28 newborns with organic acidurias (36.36%, 1:5220), and 12 newborns and two mothers with FAO disorders (18.18%; 1:10439 or 1:12179 without mothers). Phenylketonuria and methylmalonic acidemia were the two most common disorders, accounting for 65.33% (49/75) of all confirmed newborn. Some hotspot mutations were observed for several IEMs, including PAH gene c.728G>A for phenylketonuria; MMACHC gene c.609G>A and c.567dupT, MMUT gene c.323G>A for methylmalonic acidemia and SLC25A13 gene c.852_855del for citrin deficiency. Our study provides effective clinical guidance for the popularization and application of expanded newborn screening, genetic screening, and genetic counseling of IEMs in this region.

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