Abstract P170: High Intake of Dietary Protein is Associated With Increased Risk of Heart Failure With Preserved Ejection Fraction
Bibliographic record
Abstract
Background: Heart Failure with preserved ejection fraction(HFpEF) is growing and common problem in elderly women with high morbidity and mortality. The role of diet in its prevention is under-researched. Objective: To evaluate calibrated and uncalibrated dietary protein, energy adjusted animal and vegetable protein and their association with incident HFpEF. Methods: Study participants in Women’s Health Initiatve (n=14,184) with valid FFQ data, free of baseline HF or missing covariates had urinary nitrogen calibrated total protein, energy calibrated using doubly labelled water animal and vegetable protein determined. Cox models adjusted for age, education, race/ethnicity, CHD, diabetes,waist to hip ration,hypertension, physical activity, systolic blood pressure, anemia, atrial fibrillation evaluated prospective association with HFpEF. HFpEF was defined as ejection fraction >45%. Results: Over 13.2 years of follow-up, there were 513 cases of HF, 268 cases of HFpEF, 162 of HFrEF and 83 of undetermined ejection fraction. Increasing levels of calibrated total protein showed a linear dose response relationship with an increased risk of HFpEF whereas uncalibrated total protein did not. Higher intake of animal protein demonstrated an increased risk , while vegetable protein showed a trend towards and inverse relationship. (see table) Conclusion: HIgher levels of calibrated total protein are associated with increased risk of HFpEF with animal protein appearing to be the most deleterious. Plant based diets with high levels of vegetable protein may be protective. Further studies should evaluate this.
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 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
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.
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.
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".