Protein Intake and Disability Trajectories in Very Old Adults: The Newcastle 85+ Study
Bibliographic record
Abstract
OBJECTIVES: To determine whether protein intake is associated with better disability trajectories in the oldest adults (≥85) and whether muscle mass and muscle strength would partially mediate this. DESIGN: Prospective cohort study. SETTING: Newcastle-upon-Tyne and North Tyneside, United Kingdom. PARTICIPANTS: Community-dwelling older adults aged 85 at baseline (N=722). METHODS: Protein intake was estimated using two 24-hour multiple-pass recalls at baseline. Disability was measured as difficulty performing 17 activities of daily living at baseline and 18, 36, and 60 months. Trajectories were derived using mortality-adjusted group-based trajectory modelling. The effect of protein intake (g/kg of adjusted body weight (aBW)/d) on disability trajectories was examined using multinomial logistic regression. RESULTS: Participants had 4 distinct disability trajectories (between the ages of 85 and 90: constant very low (AT1), mild (AT2), moderate (AT3), and severe (AT4). Each unit increase in protein (g) per kg of aBW/d was associated with greater odds of AT1 (odds ratio (OR=7.97, 95% confidence interval (CI)=1.96-32.43, p = .004) and AT2 (OR=3.28, 95% CI=1.09-9.87, p = .03) than of AT4 over 5 years in models adjusted for selected covariates. Participants with protein intake of 1.0 g/kg aBW/d or more were more likely to belong to AT1 (OR=3.65, 95% CI=1.59-8.38, p = .009) and AT2 (OR=2.12, 95% CI=1.16-3.90, p = .01) than to AT4. CONCLUSION: Higher protein intake, especially 1.0 g/kg aBW/d or more, was associated with better disability trajectories in the oldest adults. These findings will inform new dietary strategies to support active, healthy ageing. J Am Geriatr Soc 67:50-56, 2019.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".