Functional impact of high protein intake on healthy elderly people
Bibliographic record
Abstract
Decline in muscle mass, protein synthesis, and mitochondrial function occurs with age, and amino acids are reported to enhance both muscle protein synthesis and mitochondrial function. It is unclear whether increasing dietary protein intake corrects postabsorptive muscle changes in aging. We determined whether a 10-day diet of high [HP; 3.0 g protein x kg fat-free mass (FFM)(-1) x day(-1)] vs. usual protein intake (UP; 1.5 g protein x kg FFM(-1) x day(-1)) favorably affects mitochondrial function, protein metabolism, and nitrogen balance or adversely affects insulin sensitivity and glomerular filtration rate (GFR) in 10 healthy younger (24+/-1 yr) and 9 older (70+/-2 yr) participants in a randomized crossover study. Net daily nitrogen balance increased equally in young and older participants, but postabsorptive catabolic state also increased, as indicated by higher whole body protein turnover and leucine oxidation with no change in protein synthesis. Maximal muscle mitochondrial ATP production rate was lower in older people, with no change occurring in diet. GFR was lower in older people, and response to HP was significantly different between the two groups, with a significant increase occurring only in younger people, thus widening the differences in GFR between the young and older participants. In conclusion, a short-term high-protein diet increased net daily nitrogen balance but increased the postabsorptive use of protein as a fuel. HP did not enhance protein synthesis or muscle mitochondrial function in either young or older participants. Additionally, widening differences in GFR between young and older patients is a potential cause of concern in using HP diet in older people.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".