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Enregistrement W2132554097 · doi:10.1002/14651858.cd007469.pub2

Vitamin D supplementation for prevention of cancer in adults

2014· review· en· W2132554097 sur OpenAlexaff
Goran Bjelaković, Lise Lotte Gluud, Dimitrinka Nikolova, Kate Whitfield, Jelena Krstić, Jørn Wetterslev, Christian Gluud

Notice bibliographique

RevueCochrane Database of Systematic Reviews · 2014
Typereview
Langueen
DomaineMedicine
ThématiqueVitamin D Research Studies
Établissements canadiensFraser Health
Organismes subventionnairesnon disponible
Mots-clésCancer preventionMedicineCancerVitamin D and neurologyGerontologyOncologyInternal medicine

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: The evidence on whether vitamin D supplementation is effective in decreasing cancers is contradictory. OBJECTIVES: To assess the beneficial and harmful effects of vitamin D supplementation for prevention of cancer in adults. SEARCH METHODS: We searched the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, EMBASE, LILACS, Science Citation Index Expanded, and the Conference Proceedings Citation Index-Science to February 2014. We scanned bibliographies of relevant publications and asked experts and pharmaceutical companies for additional trials. SELECTION CRITERIA: We included randomised trials that compared vitamin D at any dose, duration, and route of administration versus placebo or no intervention in adults who were healthy or were recruited among the general population, or diagnosed with a specific disease. Vitamin D could have been administered as supplemental vitamin D (vitamin D₃ (cholecalciferol) or vitamin D₂ (ergocalciferol)), or an active form of vitamin D (1α-hydroxyvitamin D (alfacalcidol), or 1,25-dihydroxyvitamin D (calcitriol)). DATA COLLECTION AND ANALYSIS: Two review authors extracted data independently. We conducted random-effects and fixed-effect model meta-analyses. For dichotomous outcomes, we calculated the risk ratios (RRs). We considered risk of bias in order to assess the risk of systematic errors. We conducted trial sequential analyses to assess the risk of random errors. MAIN RESULTS: Eighteen randomised trials with 50,623 participants provided data for the analyses. All trials came from high-income countries. Most of the trials had a high risk of bias, mainly for-profit bias. Most trials included elderly community-dwelling women (aged 47 to 97 years). Vitamin D was administered for a weighted mean of six years. Fourteen trials tested vitamin D₃, one trial tested vitamin D₂, and three trials tested calcitriol supplementation. Cancer occurrence was observed in 1927/25,275 (7.6%) recipients of vitamin D versus 1943/25,348 (7.7%) recipients of control interventions (RR 1.00 (95% confidence interval (CI) 0.94 to 1.06); P = 0.88; I² = 0%; 18 trials; 50,623 participants; moderate quality evidence according to the GRADE instrument). Trial sequential analysis (TSA) of the 18 vitamin D trials shows that the futility area is reached after the 10th trial, allowing us to conclude that a possible intervention effect, if any, is lower than a 5% relative risk reduction. We did not observe substantial differences in the effect of vitamin D on cancer in subgroup analyses of trials at low risk of bias compared to trials at high risk of bias; of trials with no risk of for-profit bias compared to trials with risk of for-profit bias; of trials assessing primary prevention compared to trials assessing secondary prevention; of trials including participants with vitamin D levels below 20 ng/mL at entry compared to trials including participants with vitamin D levels of 20 ng/mL or more at entry; or of trials using concomitant calcium supplementation compared to trials without calcium. Vitamin D decreased all-cause mortality (1854/24,846 (7.5%) versus 2007/25,020 (8.0%); RR 0.93 (95% CI 0.88 to 0.98); P = 0.009; I² = 0%; 15 trials; 49,866 participants; moderate quality evidence), but TSA indicates that this finding could be due to random errors. Cancer occurrence was observed in 1918/24,908 (7.7%) recipients of vitamin D₃ versus 1933/24,983 (7.7%) in recipients of control interventions (RR 1.00 (95% CI 0.94 to 1.06); P = 0.88; I² = 0%; 14 trials; 49,891 participants; moderate quality evidence). TSA of the vitamin D₃ trials shows that the futility area is reached after the 10th trial, allowing us to conclude that a possible intervention effect, if any, is lower than a 5% relative risk reduction. Vitamin D₃ decreased cancer mortality (558/22,286 (2.5%) versus 634/22,206 (2.8%); RR 0.88 (95% CI 0.78 to 0.98); P = 0.02; I² = 0%; 4 trials; 44,492 participants; low quality evidence), but TSA indicates that this finding could be due to random errors. Vitamin D₃ combined with calcium increased nephrolithiasis (RR 1.17 (95% CI 1.03 to 1.34); P = 0.02; I² = 0%; 3 trials; 42,753 participants; moderate quality evidence). TSA, however, indicates that this finding could be due to random errors. We did not find any data on health-related quality of life or health economics in the randomised trials included in this review. AUTHORS' CONCLUSIONS: There is currently no firm evidence that vitamin D supplementation decreases or increases cancer occurrence in predominantly elderly community-dwelling women. Vitamin D₃ supplementation decreased cancer mortality and vitamin D supplementation decreased all-cause mortality, but these estimates are at risk of type I errors due to the fact that too few participants were examined, and to risks of attrition bias originating from substantial dropout of participants. Combined vitamin D₃ and calcium supplements increased nephrolithiasis, whereas it remains unclear from the included trials whether vitamin D₃, calcium, or both were responsible for this effect. We need more trials on vitamin D supplementation, assessing the benefits and harms among younger participants, men, and people with low vitamin D status, and assessing longer duration of treatments as well as higher dosages of vitamin D. Follow-up of all participants is necessary to reduce attrition bias.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,005
score de la tête « metaresearch » (Gemma)0,005
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Revue systématique · Signal consensuel: Revue systématique
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,108
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0050,005
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0090,001
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,120
Tête enseignante GPT0,475
Écart entre enseignants0,356 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeRevue systématique
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations212
Publié2014
Routes d'admission1
Résumé présentoui

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