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Enregistrement W4401825270 · doi:10.1016/s2213-8587(24)00179-7

Meat consumption and incident type 2 diabetes: an individual-participant federated meta-analysis of 1·97 million adults with 100 000 incident cases from 31 cohorts in 20 countries

2024· review· en· W4401825270 sur OpenAlexaff
Chunxiao Li, Tom Bishop, Fumiaki Imamura, Stephen J. Sharp, Matthew Pearce, Søren Brage, Ken K. Ong, Habibul Ahsan, Maira Bes‐Rastrollo, Joline W. J. Beulens, Nicolette R. den Braver, Liisa Byberg, Scheine Canhada, Zhengming Chen, Hsin‐Fang Chung, Adrián Cortés-Valencia, Luc Djoussé, Jean‐Philippe Drouin‐Chartier, Huaidong Du, Shufa Du, Bruce Bartholow Duncan, J. Michael Gaziano, Penny Gordon-Larsen, Atsushi Goto, Fahimeh Haghighatdoost, Tommi Härkänen, Maryam Hashemian, Frank B. Hu, Till Ittermann, Ritva Järvinen, Maria Kakkoura, Nithya Neelakantan, Paul Knekt, Martín Lajous, Yanping Li, Dianna J. Magliano, Reza Malekzadeh, Loı̈c Le Marchand, Pedro Marques‐Vidal, Miguel Ángel Martínez‐González, Gertraud Maskarinec, Gita D. Mishra, Noushin Mohammadifard, Gráinne O’Donoghue, Donal J. O’Gorman, Barry Popkin, Hossein Poustchi, Nizal Sarrafzadegan, Norie Sawada, María Inês Schmidt, Jonathan E. Shaw, Sabita S. Soedamah‐Muthu, Dalia Stern, Lin Tong, Rob M. van Dam, Henry Völzke, Walter C. Willett, Alicja Wolk, Canqing Yu, Nita G. Forouhi, Nicholas J. Wareham

Notice bibliographique

RevueThe Lancet Diabetes & Endocrinology · 2024
Typereview
Langueen
DomaineMedicine
ThématiqueNutritional Studies and Diet
Établissements canadiensUniversité Laval
Organismes subventionnairesEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Cancer InstituteNational Key Research and Development Program of ChinaEuropean Regional Development FundInstituto de Salud Carlos IIIMedical Research CouncilSeventh Framework ProgrammeKadoorie Charitable FoundationChinese Society of Clinical OncologyNIHR Cambridge Biomedical Research CentreTehran University of Medical Sciences and Health ServicesMinistry of Health, Labour and WelfareNational Institute on AgingNational Institute for Health and Care ResearchDepartment of Health and Aged Care, Australian GovernmentEuropean CommissionUniversity of OxfordBritish Heart FoundationWellcome TrustCancer Research UKNational Natural Science Foundation of ChinaVetenskapsrådetAustralian GovernmentConsejo Nacional de Ciencia y TecnologíaNational Cancer CenterCentre International de Recherche sur le CancerGlaxoSmithKlineFP7 HealthCancer Council VictoriaOffice of Research and DevelopmentU.S. Department of Veterans AffairsSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Institute of Diabetes and Digestive and Kidney DiseasesNIHR Biomedical Research Centre, Royal Marsden NHS Foundation Trust/Institute of Cancer ResearchNational Science FoundationAmerican Diabetes AssociationNational Institutes of HealthHealth Services Research and DevelopmentBloomberg Philanthropies
Mots-clésMedicineType 2 diabetesMeta-analysisConsumption (sociology)Environmental healthDiabetes mellitusMEDLINEGerontologyDemographyInternal medicineEndocrinology

Résumé

récupéré en direct d'OpenAlex

Background Meat consumption could increase the risk of type 2 diabetes. However, evidence is largely based on studies of European and North American populations, with heterogeneous analysis strategies and a greater focus on red meat than on poultry. We aimed to investigate the associations of unprocessed red meat, processed meat, and poultry consumption with type 2 diabetes using data from worldwide cohorts and harmonised analytical approaches. Methods This individual-participant federated meta-analysis involved data from 31 cohorts participating in the InterConnect project. Cohorts were from the region of the Americas (n=12) and the Eastern Mediterranean (n=2), European (n=9), South-East Asia (n=1), and Western Pacific (n=7) regions. Access to individual-participant data was provided by each cohort; participants were eligible for inclusion if they were aged 18 years or older and had available data on dietary consumption and incident type 2 diabetes and were excluded if they had a diagnosis of any type of diabetes at baseline or missing data. Cohort-specific hazard ratios (HRs) and 95% CIs were estimated for each meat type, adjusted for potential confounders (including BMI), and pooled using a random-effects meta-analysis, with meta-regression to investigate potential sources of heterogeneity. Findings Among 1 966 444 adults eligible for participation, 107 271 incident cases of type 2 diabetes were identified during a median follow-up of 10 (IQR 7–15) years. Median meat consumption across cohorts was 0–110 g/day for unprocessed red meat, 0–49 g/day for processed meat, and 0–72 g/day for poultry. Greater consumption of each of the three types of meat was associated with increased incidence of type 2 diabetes, with HRs of 1·10 (95% CI 1·06–1·15) per 100 g/day of unprocessed red meat ( I 2 =61%), 1·15 (1·11–1·20) per 50 g/day of processed meat ( I 2 =59%), and 1·08 (1·02–1·14) per 100 g/day of poultry ( I 2 =68%). Positive associations between meat consumption and type 2 diabetes were observed in North America and in the European and Western Pacific regions; the CIs were wide in other regions. We found no evidence that the heterogeneity was explained by age, sex, or BMI. The findings for poultry consumption were weaker under alternative modelling assumptions. Replacing processed meat with unprocessed red meat or poultry was associated with a lower incidence of type 2 diabetes. Interpretation The consumption of meat, particularly processed meat and unprocessed red meat, is a risk factor for developing type 2 diabetes across populations. These findings highlight the importance of reducing meat consumption for public health and should inform dietary guidelines. Funding The EU, the Medical Research Council, and the National Institute of Health Research Cambridge Biomedical Research Centre.

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)0,000
score de la tête « metaresearch » (Gemma)0,000
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: Méta-analyse · Signal consensuel: Méta-analyse
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,293
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0060,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
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 Opus0,179
Tête enseignante GPT0,372
Écart entre enseignants0,193 · 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'étudeMéta-analyse
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

Citations76
Publié2024
Routes d'admission1
Résumé présentoui

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