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Record W4401219136 · doi:10.1080/02770903.2024.2380515

Causal effects of genetically determined blood metabolites on asthma: a bidirectional Mendelian randomization study

2024· article· en· W4401219136 on OpenAlexaboutno aff
Liyu Lin, Zilun Wu, Anqi Zhong, Haocheng Luo, Wenjie Xu, Wen Luo

Bibliographic record

VenueJournal of Asthma · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMetabolomics and Mass Spectrometry Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMendelian randomizationAsthmaMedicineObservational studyRandomizationAssociation (psychology)Randomized controlled trialInternal medicineGeneticsGeneGenotypeGenetic variantsBiologyPsychology

Abstract

fetched live from OpenAlex

Objective The observational association between blood metabolites and asthma has been extensively studied. However, it is still unclear whether this association is causal. In this study, we aimed to investigate the causal relationship between blood metabolites and asthma using a bidirectional Mendelian randomization (MR) analysis. Additionally, we aimed to explore the potential mechanisms underlying this relationship.Methods The study design involved the use of genetic instruments as instrumental variables (IVs) to fulfill the assumptions of MR analysis. The data on 1,091 metabolites and 309 metabolite ratios were obtained from the Canadian Longitudinal Study on Aging (CLSA), while the data on asthma were obtained from the Integrative Epidemiology Unit (IEU) Open GWAS Project. Utilizing the inverse variance-weighted (IVW) method as the primary MR analysis approach, sensitivity tests were conducted to assess the reliability of the findings, which involved employing Cochran’s Q and the MR-Egger intercept. Furthermore, Bayesian weighted MR was used to further test the robustness of the results. Additionally, pathway analysis was conducted to explore the metabolic explanations underlying asthma.Result In our study, a comprehensive MR Analysis identified 10 metabolites and 6 metabolite ratios significantly associated with the development of asthma (FDR < 0.05). The metabolites included glycerophosphocholines(GPCs), glycerophosphoethanolamines(GPEs), and an unknown metabolite. Of these, 1-arachidonoyl-GPC, 1-myristoyl-2-arachidonoyl-GPC, 1-palmitoyl-2-arachidonoyl-GPC, and 1-(1-enyl-palmitoyl)-2-arachidonoyl-GPC were associated with an increased risk of asthma, whereas 1,2-dilinoleoyl-GPC, 1-palmitoyl-2-linoleoyl-GPC, 1,2-dilinoleoyl-GPE, 1 − oleoyl − 2 − linoleoyl − GPE, 1-palmitoyl-2-linoleoyl-GPE, and X-21470 were found to have a protective effect. No heterogeneity and pleiotropy were observed in the significant metabolites (p > 0.05), and each metabolite exhibited a consistent effect direction across all five methods. BWMR analysis results confirmed the significance and direction of effects across exposures, except for Cholesterol to linoleoyl-arachidonoyl-glycerol ratio(p = 0.673). Pathway analysis suggests that glycerophospholipid metabolism may potentially be a mechanism underlying the development of asthma.Conclusion Our MR findings suggest that the identified metabolites and pathways can serve as biomarkers for clinical asthma screening and prevention, while also providing new insights for future mechanistic exploration and drug target selection.

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.

How this classification was reachedexpand

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.501

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 communication0.0000.000
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 head0.006
GPT teacher head0.251
Teacher spread0.245 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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