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Record W4410325400 · doi:10.1186/s12889-025-22479-9

Portfolio diet and LDL-C in a young, multiethnic cohort: cross-sectional analyses with cumulative exposure modeling

2025· article· en· W4410325400 on OpenAlexafffundabout
Victoria Chen, Laura Chiavaroli, Andrea J. Glenn, Meaghan E Kavanagh, Tara Zeitoun, Seyed Mohammad Mahdavi, Cyril W.C. Kendall, David J.A. Jenkins, Ahmed El‐Sohemy, John L. Sievenpiper

Bibliographic record

VenueBMC Public Health · 2025
Typearticle
Languageen
FieldMedicine
TopicNutritional Studies and Diet
Canadian institutionsUniversity of TorontoUniversity of SaskatchewanSt. Michael's Hospital
FundersBanting and Best Diabetes Centre, University of TorontoCanadian Institutes of Health ResearchPhysicians' Services Incorporated FoundationGovernment of CanadaUniversity of TorontoCanadian Nutrition SocietyCanadian Diabetes Association
KeywordsMedicineConfoundingCross-sectional studyDemographyInternal medicineCohortCohort studyBiostatisticsEpidemiologyPopulationEnvironmental health

Abstract

fetched live from OpenAlex

Abstract Background The Portfolio Diet is a plant-based dietary pattern of cholesterol-lowering foods that has demonstrated clinically meaningful reductions in low-density lipoprotein cholesterol (LDL-C) and other cardiovascular risk factors. However, the Portfolio Diet has not been assessed in an ethnoculturally diverse population of young adults. Objective To examine the association of the Portfolio Diet Score (PDS) with LDL-C and other established cardiovascular risk factors in a young adult population. Methods This cross-sectional analysis included 1,507 men and women (mean age, 23 ± 3 years) of diverse ethnocultural backgrounds from the Toronto Nutrigenomics and Health Study. Diet was assessed by a validated Toronto-modified Harvard 196-item food frequency questionnaire with adherence to the Portfolio Diet measured using the Portfolio Diet Score. Data were analyzed using multiple linear regressions with adjustment for potential confounders. Modeling analyses related LDL-C levels according to absolute adherence to the Portfolio Diet with cumulative LDL-C and onset of rising cardiovascular risk by age. Results Participants were Caucasian (49%), East Asian (34%), South Asian (11%), or other (7%) with a mean LDL-C of 2.3 ± 0.7mmol/L. A 1-point higher PDS and higher PDS tertiles were associated with lower LDL-C (ß [95% CI] per 1-point: -0.009mmol/L [-0.016, -0.002], P = 0.013; P trend across tertiles =0.040), non-HDL-C (-0.010mmol/L [-0.018, -0.002], P = 0.014; P trend =0.028), total cholesterol (-0.011mmol/L [-0.019, -0.003], P = 0.011; P trend =0.038), systolic blood pressure (-0.150mmHg [-0.250, -0.050], P = 0.003; P trend <0.001) and diastolic blood pressure (-0.133mmHg [-0.219, -0.046], P = 0.003; P trend <0.001). Higher PDS tertiles were associated with lower triglycerides (P trend =0.039). A 1-point higher PDS was also associated with lower BMI (-0.038 kg/m 2 [-0.071, -0.004], P = 0.026), waist circumference (-0.092cm [-0.171, -0.013], P = 0.022), body weight (-0.124 kg [-0.229, -0.019], P = 0.021) and FMI (-0.019 kg/m 2 [-0.037, -0.001], P =0.039). There was no association with HDL-C, CRP, or fasting glucose. Modeling analyses suggest that compared to low adherence, 50% and 100% adherence to the Portfolio Diet may delay the onset of rising cardiovascular risk by an estimated 6 and 13 years, respectively. Conclusions Among young adults, the PDS was inversely associated with LDL-C and several other established cardiovascular risk factors. Early adherence to the Portfolio Diet may limit lifetime exposure to LDL-C and could delay the age at which cardiovascular events begin.

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 machine prediction

Teacher imitation

Not 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)0.006
metaresearch head score (Gemma)0.007
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 score0.056
Threshold uncertainty score0.111

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.003
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.116
GPT teacher head0.422
Teacher spread0.306 · 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 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".

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Citations0
Published2025
Admission routes3
Has abstractyes

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