Evidence map›Paper›PMID 37036266›Full record

ArticleCurrent protocols2023

Clinical Untargeted Metabolomics as a Functional Screen to Improve Variant Classification.

Charul Gijavanekar, Sarah H Elsea

Abstract read
In one paragraph

Article in Current protocols, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Article
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

2 authors.

Charul GijavanekarDepartment of Molecular & Human Genetics, Baylor College of Medicine, Houston, Texas.
Sarah H ElseaDepartment of Molecular & Human Genetics, Baylor College of Medicine, Houston, Texas.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the rapid increase in clinical exome and genome sequencing, the number of variants of uncertain significance (VUS) that are reported continues to rise, which poses a significant barrier to interpretation of genetic findings. For metabolic disorders, biochemical testing can help alleviate this burden of variant interpretation by providing functional validation of uncertain genetic findings in many cases. However, a major limitation of traditional biochemical testing is the targeted, narrow range of analytes clinically available, resulting in delays in diagnosis if testing is negative. Untargeted metabolomic screening offers higher diagnostic yield and assays for thousands of metabolites across multiple metabolic pathways in a single test, saving time and resources for patients, families, and physicians. When integrated with exome or genome sequencing, untargeted metabolomic screening improves diagnostic outcomes by providing functional validation of genetic findings, particularly for VUS. Here, we present representative cases across the breadth of metabolic pathways as examples of the utility of metabolomics in genomic variant classification. © 2023 Wiley Periodicals LLC.

Indexed as

ExomeMetabolomicsHumansMetabolic Networks and PathwaysUncertaintybiochemical geneticsfunctional screenfunctional validationuntargeted metabolomicsvariant classificationvariants of uncertain significance

Identifiers

PMID37036266
PMCPMC13528271

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.