Evidence map›Paper›PMID 42279586›Full record

ReviewDiagnostics (Basel, Switzerland)2026

Emerging Quantitative Targeted Metabolomics Approaches for Future Inborn Errors of Metabolism Screening.

Shayma A Osman, Ahmed Malki, Haya Al-Sulaiti, Osama Y Al-Dirbashi, Mohamed A Elrayess

Abstract readReview
In one paragraph

Review in Diagnostics (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Shayma A OsmanBiomedical Sciences Department, College of Health Sciences, QU Health, Qatar University, Doha P.O. Box 2713, Qatar.ORCID 0000-0001-8652-8583
Ahmed MalkiBiomedical Sciences Department, College of Health Sciences, QU Health, Qatar University, Doha P.O. Box 2713, Qatar.
Haya Al-SulaitiBiomedical Sciences Department, College of Health Sciences, QU Health, Qatar University, Doha P.O. Box 2713, Qatar.ORCID 0009-0001-5866-9351
Osama Y Al-DirbashiBiomedical Sciences Department, College of Health Sciences, QU Health, Qatar University, Doha P.O. Box 2713, Qatar.
Mohamed A ElrayessBiomedical Research Center, QU Health, Qatar University, Doha P.O. Box 2713, Qatar.ORCID 0000-0003-3803-4604

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Inborn errors of metabolism (IEMs) are a group of inherited genetic conditions that, in general, result from a specific enzyme defect. Clinical consequences caused by abnormal enzyme levels often disrupt affected metabolic pathways and their intermediary metabolites. Because diagnostic outcomes depend on early intervention, a timely and accurate diagnosis is essential. Quantitative targeted metabolomics (QTM) is an analytical approach that quantifies predefined metabolites and generates interpretable biochemical phenotypes. Instead of focusing solely on screening, expanded QTM methods enable higher coverage with multi-analyte profiling that can provide more comprehensive characterization of disease-associated metabolic perturbations, particularly in IEMs with overlapping biochemical profiles. This review summarizes diagnostic techniques for IEMs, outlines the principles and advantages of QTM, and evaluates its established role and emerging opportunities and limitations in advancing method development and deep metabolic phenotyping to support precision medicine.

Indexed as

biomarkersclinical metabolomicsinborn errors of metabolismnewborn screeningprecision medicinequantitative targeted metabolomics

Identifiers

PMID42279586
PMCPMC13256515

What Socratic holds

Textmetadata
LicenceCC BY
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.