Evidence map›Paper›PMID 42693303›Full record

ArticleAnnals of biomedical engineering2026

Translational Study of Using FOCM/TS Metabolites for Supporting Autism Spectrum Disorder Diagnosis.

Halil Arici, Marie Causey, Soma Patra, Uwe Kruger, Cristopher Antonio Villegas Uribe, Raun Melmed, Craig Ciuk, Sophia Crisler, Sarah Marler, Allyson Witters-Cundiff and 3 more

Registry-linked trialAbstract read
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In one paragraph

Article in Annals of biomedical engineering, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04672967 (Metabolic Autism Prediction), which is not on this 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.

NCT04672967 unknown statusnot on this map

Metabolic Autism Prediction (MAP) Study

TypeobservationalSponsorBioROSA Technologies IncRan2021 to 2022Enrolled200ConditionsAutism Spectrum Disorder, Developmental DelayArmsBioROSA MAP test
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

13 authors.

Halil AriciDepartment of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA.ORCID http://orcid.org/0009-0004-9872-7618
Marie CauseyBioROSA Technologies, Inc., Boston, MA, USA.
Soma PatraDepartment of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA.
Uwe KrugerDepartment of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA.
Cristopher Antonio Villegas UribeDepartment of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA.
Raun MelmedMelmed Center/Cortica Care, Scottsdale, Arizona, USA.
Craig CiukMelmed Center/Cortica Care, Scottsdale, Arizona, USA.
Sophia CrislerMelmed Center/Cortica Care, Scottsdale, Arizona, USA.
Sarah MarlerDepartment of Psychiatry, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Allyson Witters-CundiffDepartment of Psychiatry, Vanderbilt University Medical Center, Nashville, Tennessee, USA.
Sanjeev BhadresaBioROSA Technologies, Inc., Boston, MA, USA.
John SlatteryBioROSA Technologies, Inc., Boston, MA, USA.
Juergen HahnDepartment of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA. hahnj@rpi.edu.ORCID http://orcid.org/0000-0002-1078-4203

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeSeveral clinical trial studies have shown correlations between certain physiological measurements and an ASD diagnosis. Such findings, however, have generally not resulted in tangible progress toward practical translation due to a number of factors which this work seeks to address.

methodsThis paper presents a double-blind case/control trial design in which metabolic profiles, collected at two developmental pediatric clinics, were collected from children on a diagnostic waitlist for the purpose of developing a blood-based test for ASD. Besides obtaining blood samples, the children underwent gold-standard clinical evaluations, including the Autism Diagnostic Observation Schedule (ADOS), Mullen Scales of Early Learning (MSEL), and Vineland Adaptive Behavior Scale (VABS). The analysis, together with a complete medical history and physical exam, allowed confirmation or ruling out of suspected ASD using DSM-5 criteria. The study was based on a cohort of 140 children between the ages of 18 and 60 months that were referred to a developmental pediatrician because of concerns in their development.

results114 of these children received an ASD diagnosis, while 26 were diagnosed with non-ASD-related developmental delays. Based on the measured metabolites, artificial intelligence-based classification algorithms allowed for an over 80% accuracy in predicting whether a sample came from a child diagnosed with ASD or not.

conclusionWhile these results need to be replicated in a larger study, especially involving more children with non-ASD-related developmental delays, this work uses physiological measurements, coupled with AI, to support ASD diagnoses in a clinically relevant setting. The clinical trial that was part of this work was registered on clinicaltrials.gov as NCT04672967 and was entitled the Metabolic Autism Prediction (MAP) Study. The study was IRB approved by the Biomedical Research Alliance of New York (BRANY) IRB on August 12, 2021 (approval number: A21-10-282-888).

Indexed as

Autism spectrum disorderClinical studyFolate-dependent one-carbon metabolism pathwayMachine learningTranslational studyTrans-sulfuration pathway

Identifiers

PMID42693303

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Registered trials

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