Protein Intake and Muscle Strength in Older Persons: Does Inflammation Matter?
Bibliographic record
Abstract
OBJECTIVES: To examine whether protein intake is associated with change in muscle strength in older persons. Because systemic inflammation has been associated with protein catabolism, the study also evaluated whether a synergistic effect exists between protein intake and inflammatory markers on change in muscle strength. DESIGN: Longitudinal. SETTING: The Invecchiare in Chianti Study. PARTICIPANTS: Five hundred and ninety-eight older adults. MEASUREMENTS: Knee extension strength was measured at baseline (1998-2000) and during 3-year follow-up (2001-2003) using a handheld dynamometer. Protein intake was assessed using a detailed food frequency questionnaire. The inflammatory markers examined were C-reactive protein (CRP), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-α). RESULTS: The main effect of protein intake on change in muscle strength was not significant. However, a significant interaction was found between protein intake and CRP (P = .003), IL-6 (P = .049), and TNF-α (P = .02), indicating that lower protein intake was associated with greater decline in muscle strength in persons with high levels of inflammatory markers. CONCLUSION: Lower protein intake was associated with decline in muscle strength in persons with high levels of inflammatory markers. These results may help to understand the factors contributing to decline in muscle strength with aging and to identify the target population of older persons who may benefit from nutritional interventions aimed at preventing or reducing age-associated muscle impairments and its detrimental consequences.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".