Animal and plant protein usual intakes are not adversely associated with all-cause, cardiovascular disease–, or cancer-related mortality risk: an NHANES III analysis
Bibliographic record
Abstract
We used data from NHANES 1988–1994 to examine associations between animal and plant protein usual intakes and IGF-1 concentration with mortality from all causes, cancer, and cardiovascular disease (CVD). Adult data ( N = 15 937) were linked with mortality data ( N = 3843 events) through 2006. Usual intakes for protein were estimated using the multivariate Markov Chain Monte Carlo method. Hazard ratio (HR) models were fit for mortality types (all-cause, cancer, and CVD) with protein intake measures (per 1 g increase) and IGF-1 concentration ( N = 5753). There were no associations between animal protein (HR = 0.99; 95% confidence interval (CI): 0.98–1.01; P = 0.29) or plant protein (HR = 1.02; 95% CI: 0.95–1.10; P = 0.55) intake for all-cause mortality. Similar results were seen for CVD mortality and animal protein (HR = 1.02; 95% CI: 0.99–1.04; P = 0.14) and plant protein (HR = 1.01; 95% CI: 0.91–1.13; P = 0.81). There was an (inverse) association between cancer mortality and animal protein (HR = 0.95; 95% CI: 0.91–1.00; P = 0.04) but no relationship with plant protein (HR = 1.08; 95% CI: 0.93–1.24; P = 0.30). We found no association between concentrations of IGF-1 ( N = 5753) for all-cause mortality (HR = 1.00; 95% CI: 0.99–1.00; P = 0.81), CVD mortality (HR = 0.99; 95% CI: 0.99–1.00; P = 0.53) or cancer mortality (HR = 1.00; 95% CI: 0.99–1.00; P = 0.76). Our results remained unchanged when the sample was separated into younger (<65 years) and older (>65, or between 50 and 65 years) cohorts. Our data do not support the thesis that source-specific protein intake is associated with greater mortality risk; however, animal protein may be mildly protective for cancer mortality. Mortality risk was not associated with circulating IGF-1 in any age group.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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) | 0.001 | 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".