Evidence map›Paper›PMID 40963076›Full record

ArticleMetabolomics : Official journal of the Metabolomic Society2025

Parallel Offline Breath Sampling for Cross-Validated Analysis of Volatile Organic Compound Metabolites.

Eray Schulz, Mariana Maciel, Zhige Wang, Shivaum Heranjal, Xiaowen Liu, Sha Cao, Ryan F Relich, Mark Woollam, Mangilal Agarwal

Abstract read
In one paragraph

Article in Metabolomics : Official journal of the Metabolomic Society, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

9 authors.

Eray SchulzChemistry & Chemical Biology, Indiana University Indianapolis, Indianapolis, IN, USA.
Mariana MacielIntegrated Nanosystems Development Institute, Indiana University Indianapolis, Indianapolis, IN, USA.
Zhige WangDepartment of Computer Science, Tulane University School of Engineering, New Orleans, LA, USA.
Shivaum HeranjalIntegrated Nanosystems Development Institute, Indiana University Indianapolis, Indianapolis, IN, USA.
Xiaowen LiuDeming Department of Medicine, Tulane University School of Medicine, New Orleans, LA, USA.
Sha CaoBiomedical Engineering, Oregon Health & Science University School of Medicine, Portland, OR, USA.
Ryan F RelichPathology & Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN, USA.
Mark WoollamChemistry & Chemical Biology, Indiana University Indianapolis, Indianapolis, IN, USA. mwoollam@iu.edu.
Mangilal AgarwalChemistry & Chemical Biology, Indiana University Indianapolis, Indianapolis, IN, USA. agarwal@iu.edu.

Funding

Defense Threat Reduction Agency 2021-501
6 · The paper itself

Abstract

introductionVolatile organic compounds (VOCs) in breath are potential biomarkers for medical conditions that may be used for non-invasive health monitoring. One challenge that still exists is determining the fidelity of reported VOC biomarkers. The lack of universally accepted sampling methods makes it difficult to identify reliable candidates, thus allowing for the potential of false discovery.

objectivesThe purpose of this study was to robustly profile VOCs in breath samples collected from relatively healthy participants using two offline methods for collection/analysis via solid phase microextraction (SPME) coupled to gas chromatography - mass spectrometry (GC-MS).

methods158 cross-sectional volunteers provided one-time samples using two methods, one which directly sampled breath via SPME and another which collected breath in Tedlar bags. Using both methods, 10 volunteers provided an additional nine longitudinal samples. Ambient air samples were collected routinely, and a robust data processing schematic was used to ensure high quality reporting of on-breath VOCs.

resultsData screening and processing led to the identification of > 30 unique VOCs in both methods. Hierarchical clustering and correlation analyses demonstrated volatile terpene/-oids showed homologous trends in both data sets. Of the 12 VOCs identified using both methods, 11 analytes displayed statistically significant correlations (p < 0.05) in healthy breath samples. Finally, both methods were benchmarked regarding VOC reproducibility, and analyses showed that longitudinally collected samples were more reproducible compared to cross-sectional.

conclusionsThe quantitative results from both sampling methods mirrored each other, thus increasing the reliability and fidelity of VOCs reported along with the results from biostatistical analysis.

Indexed as

Volatile Organic CompoundsAdultBiomarkersBreath TestsCross-Sectional StudiesFemaleGas Chromatography-Mass SpectrometryHumansMaleMiddle AgedSolid Phase MicroextractionYoung AdultBiomarkersVolatile Organic CompoundsChemometric analysisExhaled breathGas chromatography–mass spectrometry (GC-MS)Solid phase microextraction (SPME)Volatile organic compounds (VOCs)

Identifiers

PMID40963076
PMCPMC12443928

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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.