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Record W4409963519 · doi:10.1080/02770903.2025.2499837

Linking anemia to asthma: maternal, childhood and adult perspectives from a meta-analysis of 20 studies

2025· review· en· W4409963519 on OpenAlexaboutno aff
Kaiwen Zheng, Xiang Wang

Bibliographic record

VenueJournal of Asthma · 2025
Typereview
Languageen
FieldMedicine
TopicAsthma and respiratory diseases
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAsthmaAnemiaMeta-analysisPediatricsImmunologyPsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Background Anemia has been implicated as a potential risk factor for asthma across different life stages, yet evidence remains inconsistent.Objective This meta-analysis aimed to evaluate the associations between maternal anemia and offspring asthma, childhood anemia and asthma, and adult anemia and asthma.Methods We searched PubMed, Embase, Web of Science, and Cochrane Library, identifying 20 observational studies. Study quality was assessed using the Newcastle-Ottawa Scale (NOS) and Agency for Healthcare Research and Quality (AHRQ) checklist. Heterogeneity, sensitivity, and publication bias were explored through subgroup analyses, sensitivity tests, and Egger’s test. Meta-analysis performed using Stata 17.Results Twenty studies involving over 4499364 participants were included. The results showed pooled OR for the association between anemia and asthma (OR = 1.55,95% CI: 1.36,1.77). Maternal anemia during pregnancy was associated with a modest increase in offspring asthma risk (OR = 1.19, 95% CI: 1.02–1.38), adult anemia was also linked to asthma (OR = 1.94, 95% CI: 1.14–3.30). Childhood anemia showed a stronger association with asthma (OR = 2.22, 95% CI: 1.65–2.99), though Egger’s test (p = 0.041) suggested publication bias, with an adjusted OR of 2.03 (95% CI: 1.51–2.74).Conclusions Anemia is significantly associated with an increased risk of asthma, particularly in children. These findings suggest a potential role for anemia screening in asthma management.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.420
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0080.005
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.049
GPT teacher head0.373
Teacher spread0.324 · 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.

Study designMeta-analysis
Domainnot available
GenreReview

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

Citations1
Published2025
Admission routes1
Has abstractyes

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