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ArticleMetabolomics : Official journal of the Metabolomic Society2020

Targeted metabolomics highlights perturbed metabolism in the brain of autism spectrum disorder sufferers.

Stewart F Graham, Onur Turkoglu, Ali Yilmaz, Ilyas Ustun, Zafer Ugur, Trent Bjorndhal, BeomSoo Han, Rupa Mandal, David Wishart, Ray O Bahado-Singh

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Article in Metabolomics : Official journal of the Metabolomic Society, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing 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

14 citing papers in PubMed.

  1. Review
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  5. Article
  6. Article
  7. Article
  8. Metabolomic Signatures of Autism Spectrum Disorder.Journal of personalized medicine · 2022
    Article
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  11. Article
  12. Article
  13. Review
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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

10 authors.

Stewart F GrahamOakland University-William Beaumont School of Medicine, Rochester, MI, 48309, USA. stewart.graham@beaumont.edu.
Onur TurkogluOakland University-William Beaumont School of Medicine, Rochester, MI, 48309, USA.
Ali YilmazOakland University-William Beaumont School of Medicine, Rochester, MI, 48309, USA.
Ilyas UstunWayne State University, Civil and Environmental Engineering, Detroit, MI, USA.
Zafer UgurOakland University-William Beaumont School of Medicine, Rochester, MI, 48309, USA.
Trent BjorndhalDepartment of Biological and Computing Sciences, University of Alberta, Edmonton, AB, Canada.
BeomSoo HanDepartment of Biological and Computing Sciences, University of Alberta, Edmonton, AB, Canada.
Rupa MandalDepartment of Biological and Computing Sciences, University of Alberta, Edmonton, AB, Canada.
David WishartDepartment of Biological and Computing Sciences, University of Alberta, Edmonton, AB, Canada.
Ray O Bahado-SinghOakland University-William Beaumont School of Medicine, Rochester, MI, 48309, USA.

Funding

NICHD NIH HHS HHSN275200900011C
6 · The paper itself

Abstract

introductionAutism spectrum disorder (ASD) is a group of neurodevelopmental disorders characterized by deficiencies in social interactions and communication, combined with restricted and repetitive behavioral issues.

objectivesAs little is known about the etiopathophysiology of ASD and early diagnosis is relatively subjective, we aim to employ a targeted, fully quantitative metabolomics approach to biochemically profile post-mortem human brain with the overall goal of identifying metabolic pathways that may have been perturbed as a result of the disease while uncovering potential central diagnostic biomarkers.

methodsUsing a combination of

resultsWe accurately identified and quantified 203 metabolites in post-mortem brain extracts and performed a metabolite set enrichment analyses identifying 3 metabolic pathways as significantly perturbed (p < 0.05). These include Pyrimidine, Ubiquinone and Vitamin K metabolism. Further, using a variety of machine-based learning algorithms, we identified a panel of central biomarkers (9-hexadecenoylcarnitine (C16:1) and the phosphatidylcholine PC ae C36:1) capable of discriminating between ASD and controls with an AUC = 0.855 with a sensitivity and specificity equal to 0.80 and 0.818, respectively.

conclusionFor the first time, we report the use of a multi-platform metabolomics approach to biochemically profile brain from people with ASD and report several metabolic pathways which are perturbed in the diseased brain of ASD sufferers. Further, we identified a panel of biomarkers capable of distinguishing ASD from control brains. We believe that these central biomarkers may be useful for diagnosing ASD in more accessible biomatrices.

Indexed as

MetabolomicsAutism Spectrum DisorderBrainHumansAutism spectrum disorderBiochemical pathwaysBiomarkersBrainMachine learningMetabolomics

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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.