Differences in all-cause mortality risk associated with animal and plant dietary protein sources consumption
Bibliographic record
Abstract
The relationship between protein intake and mortality is still controversial. We prospectively examined the associations of dietary protein sources with all-cause mortality risk in the Isfahan cohort study (ICS). A total of 5431 participants, aged ≥ 35 years, were enrolled in the ICS, in 2001 and followed through 2013. The frequency of protein intakes from different sources was estimated through a validated food frequency questionnaire at baseline. Any new case of death was recorded over the follow-up duration. Hazard ratio (HR)s and 95% confidence interval (CI)s were estimated through Cox proportional hazards regression models. During a median follow-up of 11.3 years, 483 deaths were documented. Higher intakes of plant proteins (HR = 0.64, 95% CI 0.46, 0.91) and animal proteins (HR = 1.52, 95% CI 1.13, 2.05) were associated with a decreased and increased risk of mortality, respectively. Additional adjustment for some mediators did not considerably affect the associations for animal protein (HR = 1.55, 95% CI 1.15, 2.09), whereas led to a tendency towards lower risk for plant protein in the top quintile compared with the bottom one (HR = 0.67, 95% CI 0.48, 0.95; P trend = 0.06). Among specific major sources, higher intakes of nuts and fish were associated with a 27% (95% CI 0.58, 0.93) and 21% (95% CI 0.62, 1.01) lower risk of mortality, respectively. The inverse association between plant protein and mortality risk might be mediated by some metabolic disorders. However, our results suggest an independent positive association for animal protein and all-cause mortality.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".