Evidence map›Paper›PMID 35264571›Full record

ArticleTranslational psychiatry2022

Untargeted metabolic analysis in dried blood spots reveals metabolic signature in 22q11.2 deletion syndrome.

Dorinde Korteling, Marco P Boks, Ania M Fiksinski, Ilja N van Hoek, Jacob A S Vorstman, Nanda M Verhoeven-Duif, Judith J M Jans, Janneke R Zinkstok

Open access · goldAbstract read
In one paragraph

Article in Translational psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
0.8field-weighted citation impact, top 32% of its field
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

8 citing papers in PubMed, 9 citations in OpenAlex.

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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

8 authors at 4 institutions in 2 countries.

Dorinde KortelingDepartment of Psychiatry and Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.ORCID 0000-0001-7862-6319
Marco P BoksDepartment of Psychiatry and Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands. m.p.m.boks@umcutrecht.nl.ORCID 0000-0001-6163-7484
Ania M FiksinskiDepartment of Psychiatry and Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.ORCID 0000-0002-8169-7721
Ilja N van HoekSection Metabolic Diagnostics, Department of Genetics, University Medical Center Utrecht, Utrecht, The Netherlands.
Jacob A S VorstmanDepartment of Psychiatry and Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.ORCID 0000-0002-1677-3126
Nanda M Verhoeven-DuifSection Metabolic Diagnostics, Department of Genetics, University Medical Center Utrecht, Utrecht, The Netherlands.ORCID 0000-0002-2016-5182
Judith J M JansSection Metabolic Diagnostics, Department of Genetics, University Medical Center Utrecht, Utrecht, The Netherlands.
Janneke R ZinkstokDepartment of Psychiatry and Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands. j.r.zinkstok@umcutrecht.nl.ORCID 0000-0002-5580-1898
University Medical Center Utrecht · NLRadboud University Nijmegen · NLUniversity of Toronto · CAUtrecht University · NL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The 22q11.2 deletion syndrome (22q11.2DS) is characterized by a well-defined microdeletion and is associated with increased risk of neurodevelopmental phenotypes including autism spectrum disorders (ASD) and intellectual impairment. The typically deleted region in 22q11.2DS contains multiple genes with the potential of altering metabolism. Deficits in metabolic processes during early brain development may help explain the increased prevalence of neurodevelopmental phenotypes seen in 22q11.2DS. However, relatively little is known about the metabolic impact of the 22q11.2 deletion, while such insight may lead to increased understanding of the etiology. We performed untargeted metabolic analysis in a large sample of dried blood spots derived from 49 22q11.2DS patients and 87 controls, to identify a metabolic signature for 22q11.2DS. We also examined trait-specific metabolomic patterns within 22q11.2DS patients, focusing on intelligence (intelligence quotient, IQ) and ASD. We used the Boruta algorithm to select metabolites distinguishing patients from controls, patients with ASD from patients without, and patients with an IQ score in the lowest range from patients with an IQ score in the highest range. The relevance of the selected metabolites was visualized with principal component score plots, after which random forest analysis and logistic regression were used to measure predictive performance of the selected metabolites. Analysis yielded a distinct metabolic signature for 22q11.2DS as compared to controls, and trait-specific (IQ and ASD) metabolomic patterns within 22q11.2DS patients. The metabolic characteristics of 22q11.2DS provide insights in biological mechanisms underlying the neurodevelopmental phenotype and may ultimately aid in identifying novel therapeutic targets for patients with developmental disorders.

Indexed as

Autism Spectrum DisorderDiGeorge SyndromeHumansIntelligenceIntelligence TestsPhenotype

Identifiers

PMID35264571
PMCPMC8907226
OpenAlexW4221022527

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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.