Evidence map›Paper›PMID 25875217›Full record

ArticleJournal of inherited metabolic disease2015

Untargeted metabolomic analysis for the clinical screening of inborn errors of metabolism.

Marcus J Miller, Adam D Kennedy, Andrea D Eckhart, Lindsay C Burrage, Jacob E Wulff, Luke A D Miller, Michael V Milburn, John A Ryals, Arthur L Beaudet, Qin Sun and 2 more

Erratum issuedAbstract read
In one paragraph

Article in Journal of inherited metabolic disease, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 127 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
127citing papers in PubMed, 2 pooled it
–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

127 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Metabolomic profiling in phenylketonuria: a systematic review of human studies.Metabolomics : Official journal of the Metabolomic Society · 2026
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67 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

12 authors.

Marcus J MillerDepartment of Molecular and Human Genetics, Medical Genetics Laboratory, Baylor College of Medicine, One Baylor Plaza, NAB2015, Houston, TX, 77030, USA.
Adam D KennedyMetabolon Inc., Durham, NC, USA.
Andrea D EckhartMetabolon Inc., Durham, NC, USA.
Lindsay C BurrageDepartment of Molecular and Human Genetics, Medical Genetics Laboratory, Baylor College of Medicine, One Baylor Plaza, NAB2015, Houston, TX, 77030, USA.
Jacob E WulffMetabolon Inc., Durham, NC, USA.
Luke A D MillerMetabolon Inc., Durham, NC, USA.
Michael V MilburnMetabolon Inc., Durham, NC, USA.
John A RyalsMetabolon Inc., Durham, NC, USA.
Arthur L BeaudetDepartment of Molecular and Human Genetics, Medical Genetics Laboratory, Baylor College of Medicine, One Baylor Plaza, NAB2015, Houston, TX, 77030, USA.
Qin SunDepartment of Molecular and Human Genetics, Medical Genetics Laboratory, Baylor College of Medicine, One Baylor Plaza, NAB2015, Houston, TX, 77030, USA.
V Reid SuttonDepartment of Molecular and Human Genetics, Medical Genetics Laboratory, Baylor College of Medicine, One Baylor Plaza, NAB2015, Houston, TX, 77030, USA.
Sarah H ElseaDepartment of Molecular and Human Genetics, Medical Genetics Laboratory, Baylor College of Medicine, One Baylor Plaza, NAB2015, Houston, TX, 77030, USA. elsea@bcm.edu.

Funding

MEDICAL GENETICS RESEARCH FELLOWSHIP PROGRAMT32GM007526 · NIGMS · BAYLOR COLLEGE OF MEDICINE · PI Brendan Lee · 1985 to 2026
$11.4M
NIGMS NIH HHS T32 GM007526NIGMS NIH HHS T32 GM07526-37
6 · The paper itself

Abstract

Global metabolic profiling currently achievable by untargeted mass spectrometry-based metabolomic platforms has great potential to advance our understanding of human disease states, including potential utility in the detection of novel and known inborn errors of metabolism (IEMs). There are few studies of the technical reproducibility, data analysis methods, and overall diagnostic capabilities when this technology is applied to clinical specimens for the diagnosis of IEMs. We explored the clinical utility of a metabolomic workflow capable of routinely generating semi-quantitative z-score values for ~900 unique compounds, including ~500 named human analytes, in a single analysis of human plasma. We tested the technical reproducibility of this platform and applied it to the retrospective diagnosis of 190 individual plasma samples, 120 of which were collected from patients with a confirmed IEM. Our results demonstrate high intra-assay precision and linear detection for the majority compounds tested. Individual metabolomic profiles provided excellent sensitivity and specificity for the detection of a wide range of metabolic disorders and identified novel biomarkers for some diseases. With this platform, it is possible to use one test to screen for dozens of IEMs that might otherwise require ordering multiple unique biochemical tests. However, this test may yield false negative results for certain disorders that would be detected by a more well-established quantitative test and in its current state should be considered a supplementary test. Our findings describe a novel approach to metabolomic analysis of clinical specimens and demonstrate the clinical utility of this technology for prospective screening of IEMs.

Indexed as

BiomarkersHumansInfant, NewbornMetabolism, Inborn ErrorsMetabolomicsNeonatal ScreeningReproducibility of ResultsRetrospective StudiesSensitivity and SpecificityBiomarkers

Identifiers

PMID25875217
PMCPMC4626538

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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.