Evidence map›Paper›PMID 38715861›Full record

ReviewAnalytical science advances2021

Applications of quantitative metabolomics to revolutionize early diagnosis of inborn errors of metabolism in India.

Jisha Chandran, Anikha Bellad, Madan Gopal Ramarajan, Kannan Rangiah

Abstract readReview
In one paragraph

Review in Analytical science advances, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Review
  2. Review
  3. Article
  4. Article
  5. Editorial for special issue: Metabolomics in India.Analytical science advances · 2021
    Article
  6. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Jisha ChandranInstitute of Bioinformatics Bangalore Karnataka India.
Anikha BelladInstitute of Bioinformatics Bangalore Karnataka India.
Madan Gopal RamarajanInstitute of Bioinformatics Bangalore Karnataka India.ORCID https://orcid.org/0000-0002-3552-3544
Kannan RangiahInstitute of Bioinformatics Bangalore Karnataka India.ORCID https://orcid.org/0000-0002-0037-4729

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

inborn errors of metabolismliquid chromatography‐mass spectrometrymetabolomicsnewborn screeningquantification

Identifiers

PMID38715861
PMCPMC10989570

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.