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Enregistrement W2750909880 · doi:10.1016/s0140-6736(17)32253-5

Fruit, vegetable, and legume intake, and cardiovascular disease and deaths in 18 countries (PURE): a prospective cohort study

2017· article· en· W2750909880 sur OpenAlexafffund
Victoria Miller, Andrew Mente, Mahshid Dehghan, Sumathy Rangarajan, Xiaohe Zhang, Sumathi Swaminathan, Gilles R. Dagenais, Rajeev Gupta, Viswanathan Mohan, Scott A. Lear, Shrikant I. Bangdiwala, Aletta E. Schutte, Edelweiss Wentzel‐Viljoen, Álvaro Avezum, Yüksel Altuntaş, Khalid Yusoff, Noor Hassim Ismail, Nasheeta Peer, Jephat Chifamba, Rafael Díaz, Omar Rahman, Noushin Mohammadifard, F. C. F. Lana, Katarzyna Zatońska, Andreas Wielgosz, Afzalhussein Yusufali, Romaina Iqbal, Patricio López‐Jaramillo, Rasha Khatib, Annika Rosengren, V. Raman Kutty, Wei Li, Jiankang Liu, Xiaoyun Liu, Lu Yin, Koon Teo, Sonia S. Anand, Salim Yusuf, Andrés Orlandini, B Linetsky, S Toscanelli, Germana Casaccia, JM Maini Cuneo, Rita Yusuf, AK Azad, Khondkar Ayaz Rabbani, HM Cherry, Adnan Mannan, I Hassan, AT Talukdar, RB Tooheen, MU Khan, Mariz Sintaha, Tarzia Choudhury, Md Rabiul Haque, S Parvin, GB Oliveira, CS Marcílio, Antônio José Cordeiro Mattos, Jane DeJesus, D Agapay, T Tongana, Rubén Solano, I. Patrick Kay, Sylvie Trottier, Julija Rimac, W Elsheikh, L Heldman, E Ramezani, Paul Poirier, Ginette Turbide, D. Auger, A LeBlanc De Bluts, MC Proulx, Marie‐Pierre Cayer, Nadine Bonneville, Danijela Gašević, E Corber, Veronica de Jong, I Vukmirovich, George Fodor, Andrew Pipe, A Shane, Fernando Laņas, Pamela Serón, S Martinez, A Valdebenito, María José Oliveros, Xingyu Wang, Wen­hua Zhao, Jian Bo, Zhao Xiuwen, Chang Xiaohong, Tao Chen, Hui Chen, Qing Deng, Xiaoru Cheng, He Xinye, Jian Li, Juan Li, Xu Liu, Ren Bing, Wang Wei, Yang Wang, Yi Zhai, Zhu Manlu, Fanghong Lu, Jianfang Wu, Yindong Li, Yan Hou, Liangqing Zhang, Baoxia Guo, Zhang Shi-ying, Bian Rongwen, Tian Xiuzhen, Dong Li, Di Chen, Wu Jianguo, Xiao Yize, Tianlu Liu, Changlin Dong, Ning Li, Xiaolan Ma, Yang Yuqing, Lei Rensheng, Fu Minfan, Jing He, Yü Liu, Xiaojie Xing, Qiang Zhou, Paul Anthony Camacho, R Garcia, LJA Jurado, Diego Gómez-Arbeláez, JF Arguello, R Dueñas, S Silva, LP Pradilla, F Ramírez, DI Molina, Carlos Cure-Cure, Suhey Pérez, E Hernandez, E. Arcos, S Fernandez, Claudia Narváez, J Paez, A Sotomayor, Henry García, G. Sánchez, T David, Prem Mony, Mário Vaz, A V Bharathi, K Shankar AV Kurpad, KG Jayachitra, Narinder Kumar, Mohan Deepa, K Parthiban, M Anitha, S Hemavathy, T Rahulashankiruthiyayan, D Anitha, K Sridevi, Rajeev Gupta, RB Panwar, Indu Mohan, Priyanka Rastogi, S. Rastogi, R. Bhargava, JS Thakur, Binod Kumar Patro, P. V. M. Lakshmi, R Mahajan, P Chaudary, Krishnapillai Vijayakumar, G Rajasree, AR Renjini, A Deepu, B. Sandhya, Stephen Asha, HS Soumya, Roya Kelishadi, Ahmad Bahonar, Hadi Heidari, KK Ng, A Devi, NM Nasir, MM Yasin, Maizatullifah Miskan, EA Rahman, MKM Arsad, Farnaza Ariffin, SA Razak, FA Majid, NA Bakar, MY Yacob, N Zainon, Ruhaya Salleh, MKA Ramli, NA Halim, SR Norlizan, NM Ghazali, MN Arshad, R Razali, Syed Hassan Ali, HR Othman, CWJCW Hafar, A Pit, Norlaila Danuri, F Basir, SNA Zahari, Hafez Mohammad Ammar Abdullah, MA Arippin, NA Zakaria, MJ Hasni, MT Azmi, MI Zaleha, KY Hazdi, AR Rizam, W Sazman, A Azman, Umaiyeh Khammash, A Khatib, Rita Giacaman, Romania Iqbal, Asad Afridi, Rehman Khawaja, Auriba Raza, Khawar Kazmi, Witold Zatoński, Andrzej Szuba, Rafał Ilow, M Ferus, Bożena Regulska−Ilow, Dorota Różańska, Maria Wołyniec, Mohammed K. Ali, Marlize Kruger, H H Voster, FC Eloff, H de Ridder, H Moss, J Potgieter, A.V. Diez Roux, Megan Watson, G de Wet, Antonel Olckers, Johann C. Jerling, Marlien Pieters, Trynke Hoekstra, Thandi Puoane, Ehimario Igumbor, Lungiswa Tsolekile, David Sanders, Pooveshni Naidoo, N Steyn, Bongani M. Mayosi, Brian Rayner, Estelle V. Lambert, Naomi Levitt, Tracy Kolbe‐Alexander, Lucas Ntyintyane, G. Hughes, Rina Swart, Jean Fourie, Moïse Muzigaba, S Xapa, N Gobile, K Ndayi, B Jwili, K Ndibaza, Bonaventure Amandi Egbujie, Kristina Bengtsson Boström, A Gustavsson, Mattias Andréasson, M Snällman, L Wirdemann, A Oguz, Neşe İmeryüz, Ahmet Temizhan, KBT Calik, AAK Akalin, OT Caklili, MV Keskinler, AN Erbakan, AM Yusufali, Wael Almahmeed, H Swidan, EA Darwish, ARA Hashemi, Najib Al‐Khaja, J. Muscat-Baron, SH Ahmed, TM Mamdouh, WM Darwish, MHS Abdelmotagali, SA Omer Awed, GA Movahedi, F Hussain, H Al Shaibani, RIM Gharabou, DF Youssef, AZS Nawati, ZAR Abu Salah, RFE Abdalla, SM Al Shuwaihi, MA Al Omairi, OD Cadigal, R.S. Alejandrino, Lenon Gwaunza, G Terera, Carol Mahachi, Pretty Murambiwa, T Machiweni, RF Mapanga

Notice bibliographique

RevueThe Lancet · 2017
Typearticle
Langueen
DomaineMedicine
ThématiqueNutritional Studies and Diet
Établissements canadiensUniversity of OttawaSimon Fraser UniversityPopulation Health Research InstituteUniversité LavalInstitut universitaire de cardiologie et de pneumologie de QuébecMcMaster University
Organismes subventionnairesFaculty of Community and Health Sciences, University of the Western CapeCanadian Institutes of Health ResearchIndependent University, BangladeshServierSerbian Academy of Sciences and ArtsUniwersytet Medyczny im. Piastów Slaskich we WroclawiuUniversiti Kebangsaan MalaysiaNorthwest UniversityAstraZenecaMinistério da Ciência, Tecnologia e InovaçãoAFA FörsäkringForskningsrådet för Arbetsliv och SocialvetenskapSvenska Forskningsrådet FormasInternational Development Research CentreMedical Research CouncilKing Saud UniversityIndian Council of Medical ResearchUniversiti Teknologi MARANational Research FoundationSouth Africa Netherlands research Programme on Alternatives in DevelopmentPublic Health Agency of CanadaUniversidad de La FronteraGlaxoSmithKlinePublic Health AgencySanofiVetenskapsrådetHeart and Stroke Foundation of CanadaDepartamento Administrativo de Ciencia, Tecnología e Innovación (COLCIENCIAS)
Mots-clésMedicineProspective cohort studyEnvironmental healthDiseaseEpidemiologyMyocardial infarctionCohort studySocioeconomic statusDemographyCohortPopulationInternal medicine

Résumé

récupéré en direct d'OpenAlex

BackgroundThe association between intake of fruits, vegetables, and legumes with cardiovascular disease and deaths has been investigated extensively in Europe, the USA, Japan, and China, but little or no data are available from the Middle East, South America, Africa, or south Asia. Methods We did a prospective cohort study (Prospective Urban Rural Epidemiology[PURE] in 135 335 individuals aged 35 to 70 years without cardiovascular disease from 613 communities in 18 low-income, middle-income, and highincome countries in seven geographical regions: North America and Europe, South America, the Middle East, south Asia, China, southeast Asia, and Africa.We documented their diet using country-specific food frequency questionnaires at baseline.Standardised questionnaires were used to collect information about demographic factors, socioeconomic status (education, income, and employment), lifestyle (smoking, physical activity, and alcohol intake), health history and medication use, and family history of cardiovascular disease.The follow-up period varied based on the date when recruitment began at each site or country.The main clinical outcomes were major cardiovascular disease (defined as death from cardiovascular causes and non-fatal myocardial infarction, stroke, and heart failure), fatal and non-fatal myocardial infarction, fatal and non-fatal strokes, cardiovascular mortality, non-cardiovascular mortality, and total mortality.Cox frailty models with random effects were used to assess associations between fruit, vegetable, and legume consumption with risk of cardiovascular disease events and mortality.Findings Participants were enrolled into the study between Jan 1, 2003, and March 31, 2013.For the current analysis, we included all unrefuted outcome events in the PURE study database through March 31, 2017.Overall, combined mean fruit, vegetable and legume intake was 3•91 (SD 2•77) servings per day.During a median 7•4 years (5•5-9•3) of followup, 4784 major cardiovascular disease events, 1649 cardiovascular deaths, and 5796 total deaths were documented.Higher total fruit, vegetable, and legume intake was inversely associated with major cardiovascular disease, myocardial infarction, cardiovascular mortality, non-cardiovascular mortality, and total mortality in the models adjusted for age, sex, and centre (random effect).The estimates were substantially attenuated in the multivariable adjusted models for major cardiovascular disease (hazard ratio [HR] 0•90, 95% CI 0•74-1•10, p trend =0•1301), myocardial infarction (0•99, 0•74-1•31; p trend =0•2033), stroke (0•92, 0•67-1•25; p trend =0•7092), cardiovascular mortality (0•73, 0•53-1•02; p trend =0•0568), non-cardiovascular mortality (0•84, 0•68-1•04; p trend =0•0038), and total mortality (0•81, 0•68-0•96; p trend <0•0001).The HR for total mortality was lowest for three to four servings per day (0•78, 95% CI 0•69-0•88) compared with the reference group, with no further apparent decrease in HR with higher consumption.When examined separately, fruit intake was associated with lower risk of cardiovascular, non-cardiovascular, and total mortality, while legume intake was inversely associated with non-cardiovascular death and total mortality (in fully adjusted models).For vegetables, raw vegetable intake was strongly associated with a lower risk of total mortality, whereas cooked vegetable intake showed a modest benefit against mortality.Interpretation Higher fruit, vegetable, and legume consumption was associated with a lower risk of non-cardiovascular, and total mortality.Benefits appear to be maximum for both non-cardiovascular mortality and total mortality at three to four servings per day (equivalent to 375-500 g/day).

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,002
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,035
Score d'incertitude au seuil0,070

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,002
Méta-épidémiologie (sens strict)0,0010,002
Méta-épidémiologie (sens large)0,0010,003
Bibliométrie0,0010,003
Études des sciences et des technologies0,0010,001
Communication savante0,0020,001
Science ouverte0,0010,002
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,0020,001

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 Opus0,025
Tête enseignante GPT0,279
Écart entre enseignants0,255 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

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

En bref

Citations654
Publié2017
Routes d'admission2
Résumé présentnon

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