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Record W3194029690 · doi:10.1289/isee.2021.o-to-158

Metabolomics and gene expression profiles in association with air pollution exposure mixtures among young adults with asthma

2021· article· en· W3194029690 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueISEE Conference Abstracts · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsMetabolomicsAsthmaTranscriptomeAir pollutionMedicineGene expressionBiologyComputational biologyEnvironmental healthBioinformaticsGeneGeneticsInternal medicineEcology

Abstract

fetched live from OpenAlex

BACKGROUND AND AIM: Air pollution exposure has been shown to increase severity of various disease outcomes including asthma control, however, the underlying biological mechanisms are not well established. In this study, we aim to leverage transcriptomics and metabolomics to identify biological mechanisms of air pollutants exposure. METHODS: In this cross-sectional study, 102 young adults with childhood asthma history, who were participants of Southern California Children’s Health Study, were enrolled in 2012. Whole blood gene expression data was measured with Illumina HumanHT-12 v4 Expression BeadChip, with 20,869 expression signatures included in the analysis. Serum untargeted metabolomics were analyzed using the Metabolon UPLC-MS/MS, and 937 metabolites were confirmed for all samples. Participants’ regional (NO2, O3, PM10, PM2.5) and near-roadway air pollution exposure were based on nearby central monitoring and modelling during one-month and one-year before the study visit. Multi-omics network analysis (R package ‘xMWAS’) was conducted to identify subnetworks that link metabolomics and transcriptomics to specific air pollutants exposure. Joint-pathway analysis based on MetaboAnalyst (McGill University) was performed to identify pathways associated with air pollutants in each subnetwork. Key covariates such as SES, ethnicity, sex, and smoking were adjusted in all analyses. RESULTS:Network analysis found that 357 gene markers, 92 metabolites, and one-year and one-month exposures to 8 air pollutants were clustered into 9 subnetworks. For the subnetwork including PM10 and one-month O3, gene expression markers were enriched in pathways for insulin secretion, antigen processing and presentation. Another subnetwork including PM2.5 and NO2 exposures was inked to altered metabolism of amino acids such as arginine, serine, and aspartic acid. One-year O3 exposure was clustered with metabolites and genes involved in glycerophospholipid metabolism and N-Glycan biosynthesis. CONCLUSIONS:This study demonstrates that exposure to various air pollutants may induce changes in gene expression and metabolomics in individuals with asthma, potentially affecting disease prognosis. KEYWORDS: Air Pollution, Metabolomics, Transcriptomics, Network Analysis, Pathway Analysis

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.547

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.238
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it