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Portfolio Diet Score and Risk of Cardiovascular Disease: Findings From 3 Prospective Cohort Studies

2023· article· en· W4387930988 on OpenAlexafffund
Andrea J. Glenn, Marta Guasch‐Ferré, Vasanti Malik, Cyril W.C. Kendall, JoAnn E. Manson, Eric B. Rimm, Walter C. Willett, Qi Sun, David J.A. Jenkins, Frank B. Hu, John L. Sievenpiper

Bibliographic record

VenueCirculation · 2023
Typearticle
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsCanada Research ChairsUniversity of TorontoUniversity of SaskatchewanSt. Michael's Hospital
FundersInstitute of Nutrition, Metabolism and DiabetesNational Cancer InstituteNational Heart, Lung, and Blood InstituteOntario Ministry of Research and InnovationAgriculture and Agri-Food CanadaEuropean Association for the Study of DiabetesUniversity of TorontoCanada Research ChairsNational Institutes of HealthAcademy of Nutrition and DieteticsCanola Council of CanadaCoca-Cola FoundationLoblaw Companies LimitedGeneral MillsHerbalife NutritionAlberta Pulse Growers CommissionHospital for Sick ChildrenAmerican Heart AssociationDanish Cancer Society Research CenterAlpro FoundationPhysicians' Services Incorporated FoundationPeanut InstituteCanadian Nutrition SocietyInternational Nut and Dried Fruit CouncilEuropean Foundation for the Study of DiabetesAlmond Board of CaliforniaDiabetes CanadaDanoneSoy Nutrition InstituteArizona State UniversityGovernment of CanadaSt. Michael's Hospital FoundationU.S. Department of AgricultureAbbott LaboratoriesKellogg'sCanadian Institutes of Health ResearchPepsiCoCalifornia Strawberry Commission
KeywordsMedicineProspective cohort studyAtherosclerotic cardiovascular diseaseDiseaseInternal medicineCohortFramingham Risk ScoreCohort studyPortfolioFinance

Abstract

fetched live from OpenAlex

BACKGROUND: The plant-based Portfolio dietary pattern includes recognized cholesterol-lowering foods (ie, plant protein, nuts, viscous fiber, phytosterols, and plant monounsaturated fats) shown to improve several cardiovascular disease (CVD) risk factors in randomized controlled trials. However, there is limited evidence on the role of long-term adherence to the diet and CVD risk. The primary objective was to examine the relationship between the Portfolio Diet Score (PDS) and the risk of total CVD, coronary heart disease (CHD), and stroke. METHODS: We prospectively followed 73 924 women in the Nurses’ Health Study (1984–2016), 92 346 women in the Nurses’ Health Study II (1991–2017), and 43 970 men in the Health Professionals Follow-up Study (1986–2016) without CVD or cancer at baseline. Diet was assessed using validated food frequency questionnaires at baseline and every 4 years using a PDS that positively ranks plant protein (legumes), nuts and seeds, viscous fiber sources, phytosterols (mg/day), and plant monounsaturated fat sources, and negatively ranks foods high in saturated fat and cholesterol. RESULTS: During up to 30 years of follow-up, 16 917 incident CVD cases, including 10 666 CHD cases and 6473 strokes, were documented. After multivariable adjustment for lifestyle factors and a modified Alternate Healthy Eating Index (excluding overlapping components), comparing the highest with the lowest quintile, participants with a higher PDS had a lower risk of total CVD (pooled hazard ratio [HR], 0.86 [95% CI, 0.81–0.92]; P trend <0.001), CHD (pooled HR, 0.86 [95% CI, 0.80–0.93]; P trend =0.0001), and stroke (pooled HR, 0.86 [95% CI, 0.78–0.95]; P trend =0.0003). In addition, a 25-percentile higher PDS was associated with a lower risk of total CVD (pooled HR, 0.92 [95% CI, 0.89–0.95]), CHD (pooled HR, 0.92 [95% CI, 0.88–0.95]), and stroke (pooled HR, 0.92 [95% CI, 0.87–0.96]). Results remained consistent across sensitivity and most subgroup analyses, and there was no evidence of departure from linearity for CVD, CHD, or stroke. In a subset of participants, a higher PDS was associated with a more favorable blood lipid and inflammatory profile. CONCLUSIONS: The PDS was associated with a lower risk of CVD, including CHD and stroke, and a more favorable blood lipid and inflammatory profile, in 3 large prospective cohorts.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.006
Threshold uncertainty score0.283

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.029
GPT teacher head0.274
Teacher spread0.245 · 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 teacher head, 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

Citations52
Published2023
Admission routes2
Has abstractyes

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