Applications of quantitative metabolomics to revolutionize early diagnosis of inborn errors of metabolism in India
Document Type
Article
Publication Title
Analytical Science Advances
Abstract
Inborn errors of metabolism (IEMs) are a group of disorders caused by disruption of metabolic pathways, which leads to accumulation, decreased circulating levels, or increased excretion of metabolites as a consequence of the underlying genetic defects. These heterogeneous groups of disorders cause significant neonatal and infant mortality across the whole world and it is of utmost concern for developing countries like India owing to lack of awareness and standard preventive strategies like newborn screening (NBS). Though the predictive cumulative incidence of IEMs is said to be ∼1:800 newborns, data pertaining to the true prevalence of individual IEMs is not available in the context of Indian population. There is a need for a large population-based study to get a clear picture of the prevalence of different IEMs. One of the best ways to screen for IEMs is by applying advanced liquid chromatography-mass spectrometry (LC-MS) technology using a quantitative metabolomics approaches such as selected or multiple reaction monitoring (SRM or MRM). Recent developments in LC-MS/MRM based quantification of marker metabolites in newborns have opened a novel opportunity to screen multiple disorders simultaneously from a minuscule volume of biological fluids. In this review article, we have highlighted how LC-MS/MRM based metabolomics approach with its high sensitivity and diagnostic capability can make an impact on the nation's public health through NBS programs.
First Page
546
Last Page
563
DOI
10.1002/ansa.202100010
Publication Date
12-1-2021
Recommended Citation
Chandran, Jisha; Bellad, Anikha; Ramarajan, Madan Gopal; and Rangiah, Kannan, "Applications of quantitative metabolomics to revolutionize early diagnosis of inborn errors of metabolism in India" (2021). Open Access archive. 2167.
https://impressions.manipal.edu/open-access-archive/2167