Evidence map›Paper›PMID 28301476›Full record

ArticlePLoS computational biology2017

Classification and adaptive behavior prediction of children with autism spectrum disorder based upon multivariate data analysis of markers of oxidative stress and DNA methylation.

Daniel P Howsmon, Uwe Kruger, Stepan Melnyk, S Jill James, Juergen Hahn

Open access · goldAbstract read
In one paragraph

Article in PLoS computational biology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 67 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
67citing papers in PubMed, 1 pooled it
9.0field-weighted citation impact, top 2% 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

67 citing papers in PubMed, 1 synthesis or guideline pooled it, 126 citations in OpenAlex.

  1. Pooled it
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  3. Review
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  7. Article
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  11. Biomarkers for autism spectrum disorder: a short review.Annals of medicine and surgery (2012) · 2024
    Review
  12. Review
  13. Article
  14. Rodent Models for ASD Biomarker Development.Advances in neurobiology · 2024
    Review
  15. Article
  16. Article
  17. Article
  18. Article
  19. Article
  20. Metabolomic Signatures of Autism Spectrum Disorder.Journal of personalized medicine · 2022
    Article

7 more citing papers are in PubMed but not listed here.

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 at 2 institutions in 1 country.

Daniel P HowsmonDepartment of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, New York, United States of America.ORCID 0000-0002-7177-1342
Uwe KrugerDepartment of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, New York, United States of America.ORCID 0000-0001-5664-9499
Stepan MelnykDepartment of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America.
S Jill JamesDepartment of Pediatrics, University of Arkansas for Medical Sciences, Little Rock, Arkansas, United States of America.
Juergen HahnDepartment of Chemical and Biological Engineering, Rensselaer Polytechnic Institute, Troy, New York, United States of America.
Rensselaer Polytechnic Institute · USUniversity of Arkansas for Medical Sciences · US

Funding

Modeling and Analysis of the Role of Microbiota Metabolites in T-Cell DifferentiationR01AI110642 · NIAID · TEXAS ENGINEERING EXPERIMENT STATION · PI ALANIZ, ROBERT CHRISTOPHER, HAHN, JUERGEN · 2015 to 2019
$2.0M
NIAID NIH HHS R01 AI110642
6 · The paper itself

Abstract

The number of diagnosed cases of Autism Spectrum Disorders (ASD) has increased dramatically over the last four decades; however, there is still considerable debate regarding the underlying pathophysiology of ASD. This lack of biological knowledge restricts diagnoses to be made based on behavioral observations and psychometric tools. However, physiological measurements should support these behavioral diagnoses in the future in order to enable earlier and more accurate diagnoses. Stepping towards this goal of incorporating biochemical data into ASD diagnosis, this paper analyzes measurements of metabolite concentrations of the folate-dependent one-carbon metabolism and transulfuration pathways taken from blood samples of 83 participants with ASD and 76 age-matched neurotypical peers. Fisher Discriminant Analysis enables multivariate classification of the participants as on the spectrum or neurotypical which results in 96.1% of all neurotypical participants being correctly identified as such while still correctly identifying 97.6% of the ASD cohort. Furthermore, kernel partial least squares is used to predict adaptive behavior, as measured by the Vineland Adaptive Behavior Composite score, where measurement of five metabolites of the pathways was sufficient to predict the Vineland score with an R2 of 0.45 after cross-validation. This level of accuracy for classification as well as severity prediction far exceeds any other approach in this field and is a strong indicator that the metabolites under consideration are strongly correlated with an ASD diagnosis but also that the statistical analysis used here offers tremendous potential for extracting important information from complex biochemical data sets.

Indexed as

Multivariate AnalysisAutism Spectrum DisorderBiomarkersChildChild, PreschoolData Interpretation, StatisticalDNA MethylationFemaleFolic AcidHumansMaleOxidative StressReproducibility of ResultsRisk FactorsSensitivity and SpecificityBiomarkersFolic Acid

Identifiers

PMID28301476
PMCPMC5354243
OpenAlexW2595641790

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.