The Effect of Elevated Protein Intake on DNA Damage in Older People: Comparative Secondary Analysis of Two Randomized Controlled Trials
Bibliographic record
Abstract
A high protein intake at old age is important for muscle protein synthesis, however, this could also trigger protein oxidation with the potential risk for DNA damage. The aim of this study was to investigate whether an increased protein intake at recommended level or well above would affect DNA damage or change levels of reduced (GSH) and oxidised glutathione (GSSG) in community-dwelling elderly subjects. These analyses were performed in two randomized intervention studies, in Austria and in New Zealand. In both randomized control trials, the mean protein intake was increased with whole foods, in the New Zealand study (n = 29 males, 74.2 ± 3.6 years) to 1.7 g/kg body weight/d (10 weeks intervention; p < 0.001)) in the Austrian study (n = 119 males and females, 72.9 ± 4.8 years) to 1.54 g/kg body weight/d (6 weeks intervention; p < 0.001)). In both studies, single and double strand breaks and as formamidopyrimidine—DNA glycosylase-sensitive sites were investigated in peripheral blood mononuclear cells or whole blood. Further, resistance to H2O2 induced DNA damage, GSH, GSSG and CRP were measured. Increased dietary protein intake did not impact on DNA damage markers and GSH/GSSG levels. A seasonal-based time effect (p < 0.05), which led to a decrease in DNA damage and GSH was observed in the Austrian study. Therefore, increasing the protein intake to more than 20% of the total energy intake in community-dwelling seniors in Austria and New Zealand did not increase measures of DNA damage, change glutathione status or elevate plasma CRP.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".