Evidence map›Paper›PMID 42078069›Full record

ArticleAnalytical science advances2026

Comprehensive Quantitative Urinary Steroid Profiling of 29 Steroids Using Liquid Chromatography-Tandem Mass Spectrometry.

Joshua T Bain, Fozia Shaheen, Alessandro Prete, Lorna C Gilligan, Angela E Taylor

Abstract read
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In one paragraph

Article in Analytical science advances, 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.

Joshua T BainDepartment of Metabolism and Systems Science, College of Medicine and Health University of Birmingham Birmingham UK.ORCID https://orcid.org/0009-0002-2983-9697
Fozia ShaheenDepartment of Metabolism and Systems Science, College of Medicine and Health University of Birmingham Birmingham UK.
Alessandro PreteDepartment of Metabolism and Systems Science, College of Medicine and Health University of Birmingham Birmingham UK.
Lorna C GilliganDepartment of Metabolism and Systems Science, College of Medicine and Health University of Birmingham Birmingham UK.
Angela E TaylorDepartment of Metabolism and Systems Science, College of Medicine and Health University of Birmingham Birmingham UK.ORCID https://orcid.org/0000-0002-5835-5643

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Steroids are critical for numerous physiological processes; disruption in their metabolism is associated with numerous endocrine disorders. Steroid quantification is essential to improve the understanding and diagnosis of these pathologies. Historically, urinary steroid profiling has been performed using low-throughput gas chromatography mass spectrometry (GC-MS), providing holistic coverage of steroid classes with low cross-reactivity. Here, we translate our previous GC-MS urinary steroid profile to a liquid chromatography tandem-MS (LC-MS/MS) platform, offering a validated, comprehensive overview of steroid metabolism with comparatively low sample preparation times and increased throughput. Urinary steroids were enzymatically hydrolysed and extracted via C

Indexed as

androgensglucocorticoidsLC‐MS/MSmineralocorticoidssteroid precursorssteroid profilingurine

Identifiers

PMID42078069
PMCPMC13132653

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Registered trials

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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.