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Associations of fats and carbohydrate intake with cardiovascular disease and mortality in 18 countries from five continents (PURE): a prospective cohort study
Mahshid Dehghan, Andrew Mente, Xiaohe Zhang, Sumathi Swaminathan, Wei Li, Viswanathan Mohan, Romaina Iqbal, Rajesh Kumar, Edelweiss Wentzel‐Viljoen, Annika Rosengren, Leela Itty Amma, Álvaro Avezum, Jephat Chifamba, Rafael Díaz, Rasha Khatib, Scott A. Lear, Patricio López‐Jaramillo, Xiaoyun Liu, Rajeev Gupta, Noushin Mohammadifard, Nan Gao, Aytekin Oğuz, Anis Safura Ramli, Pamela Serón, Yi Sun, Andrzej Szuba, Lungiswa Tsolekile, Andreas Wielgosz, Rita Yusuf, Koon Teo, Sumathy Rangarajan, Gilles R. Dagenais, Shrikant I. Bangdiwala, Shofiqul Islam, Sonia S. Anand, Salim Yusuf, Andrés Orlandini, B Linetsky, S Toscanelli, Germana Casaccia, JM Maini Cuneo, Omar Rahman, 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, S Martinez, A Valdebenito, María José Oliveros, Xingyu Wang, Wenhua Zhao, Xuan Jia, Bo Hu, Jian Bo, Zhao Xiuwen, Chang Xiaohong, Tao Chen, Hui Chen, Qing Deng, Cheng Xiaoru, 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, Shiying Zhang, 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, 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, V. Raman Kutty, K. Vijayakumar, G Rajasree, AR Renjini, A Deepu, B. Sandhya, Stephen Asha, HS Soumya, Roya Kelishadi, Ahmad Bahonar, Hadi Heidari, Khalid Yusoff, K S 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, Katarzyna Zatońska, Rafał Ilow, M Ferus, Bożena Regulska−Ilow, Dorota Różańska, Maria Wołyniec, Mohammed K. Ali, Marlize Kruger, H H Voster, Aletta E. Schutte, 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, David Sanders, Pooveshni Naidoo, N Steyn, Nasheeta Peer, 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, Neşe İmeryüz, Yüksel Altuntaş, 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
Bibliographic record
VenueThe Lancet · 2017
Typearticle
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsInstitut universitaire de cardiologie et de pneumologie de QuébecUniversity of OttawaSimon Fraser UniversityImpactMcMaster UniversityUniversité LavalPopulation Health Research Institute
FundersU.S. Department of Agriculture
KeywordsMedicineHazard ratioProspective cohort studyEpidemiologyMyocardial infarctionStroke (engine)Cohort studyDiseaseCohortProportional hazards modelInternal medicineLower riskSaturated fatConfidence intervalCholesterol
Abstract
No abstract in any covered source. Its absence is recorded, not treated as a negative.
No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.083
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 head0.030
GPT teacher head0.294
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation status
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine 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 designObservational
Domainnot available
GenreEmpirical
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
Citations1,112
Published2017
Admission routes1
Has abstractno