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Record W4407805683 · doi:10.1016/s2213-8587(24)00348-6

Vitamin D supplementation to prevent acute respiratory infections: systematic review and meta-analysis of stratified aggregate data

2025· review· en· W4407805683 on OpenAlexaff
David A. Jolliffe, Carlos A. Camargo, John Sluyter, Mary Aglipay, John F. Aloia, Peter Bergman, Heike A. Bischoff‐Ferrari, Arturo Borzutzky, Vadim Bubes, Camilla T. Damsgaard, Francine M. Ducharme, Gal Dubnov‐Raz, Susanna Esposito, Davaasambuu Ganmaa, Clare Gilham, Adit A. Ginde, Inbal Golan‐Tripto, Emma C Goodall, Cameron Grant, Chris Griffiths, Anna Maria Hibbs, Wim Janssens, Anuradha Khadilkar, Ilkka Laaksi, Margaret T. Lee, Mark Loeb, Jonathon L. Maguire, Paweł Majak, Semira Manaseki‐Holland, JoAnn E. Manson, David T. Mauger, David R. Murdoch, Akio Nakashima, Rachel Ε. Neale, Hai Pham, Christine Rake, Judy R. Rees, Jenni Rosendahl, Robert Scragg, Dheeraj Shah, Yoshiki Shimizu, Steve Simpson, Geeta Trilok‐Kumar, Mitsuyoshi Urashima, Adrian R. Martineau

Bibliographic record

VenueThe Lancet Diabetes & Endocrinology · 2025
Typereview
Languageen
FieldMedicine
TopicVitamin D Research Studies
Canadian institutionsMcMaster UniversityImpactSt. Michael's Hospital
FundersNational Center for Complementary and Integrative HealthBarts Charity
KeywordsMedicineMeta-analysisIntensive care medicineMEDLINEVitamin D and neurologyInternal medicine

Abstract

fetched live from OpenAlex

Background A 2021 meta-analysis of 37 randomised controlled trials (RCTs) of vitamin D supplementation for prevention of acute respiratory infections (ARIs) revealed a statistically significant protective effect of the intervention (odds ratio [OR] 0·92 [95% CI 0·86 to 0·99]). Since then, six eligible RCTs have been completed, including one large trial (n=15 804). We aimed to re-examine the link between vitamin D supplementation and prevention of ARIs. Methods Updated systematic review and meta-analysis of data from RCTs of vitamin D for ARI prevention using a random effects model. Subgroup analyses were done to determine whether effects of vitamin D on risk of ARI varied according to baseline 25-hydroxyvitamin D (25[OH]D) concentration, dosing regimen, or age. We searched MEDLINE, EMBASE, the Cochrane Central Register of Controlled Trials, Web of Science, and the ClinicalTrials.gov between May 1, 2020 (end-date of search of our previous meta-analysis) and April 30, 2024. No language restrictions were imposed. Double-blind RCTs supplementing vitamin D for any duration, with placebo or lower-dose vitamin D control, were eligible if approved by a Research Ethics Committee and if ARI incidence was collected prospectively and pre-specified as an efficacy outcome. Aggregate data, stratified by baseline 25(OH)D concentration and age, were obtained from study authors. The study was registered with PROSPERO (no. CRD42024527191). Findings We identified six new RCTs (19 337 participants). Data were obtained for 16 085 (83·2%) participants in three new RCTs and combined with data from 48 488 participants in 43 RCTs identified in our previous meta-analysis. For the primary comparison of any vitamin D versus placebo, the intervention did not statistically significantly affect overall ARI risk (OR 0·94 [95% CI 0·88–1·00], p=0·057; 40 studies; 61 589 participants; I 2 =26·4%). Pre-specified subgroup analysis did not reveal evidence of effect modification by age, baseline vitamin D status, dosing frequency, or dose size. Vitamin D did not influence the proportion of participants experiencing at least one serious adverse event (OR 0·96 [95% CI 0·90–1·04]; 38 studies; I 2 =0·0%). A funnel plot showed left-sided asymmetry (p=0·0020, Egger's test). Interpretation This updated meta-analysis yielded a similar point estimate for the overall effect of vitamin D supplementation on ARI risk to that obtained previously, but the 95% CI for this effect estimate now includes 1·00, indicating no statistically significant protection. Funding None.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.021
metaresearch head score (Gemma)0.047
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.026
Threshold uncertainty score0.113

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.047
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0260.038
Bibliometrics0.0110.010
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.161
GPT teacher head0.445
Teacher spread0.284 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations24
Published2025
Admission routes1
Has abstractyes

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