Protein Anabolic Responses to a Fed Steady State in Healthy Aging
Bibliographic record
Abstract
BACKGROUND: Protein anabolism in response to feeding may be impaired with aging. To determine if this could contribute to muscle loss, we studied fed-state metabolic responses in healthy, non-sarcopenic elderly women. METHODS: Whole-body [(3)H]glucose and protein ([(13)C]leucine) kinetics were measured, and muscle protein fractional synthesis rate ([(2)H(5)]phenylalanine) and signaling events were assessed from vastus lateralis biopsies in eight elderly (73 ± 3 years) and eight young women (24 ± 1 years), using a simulated fed steady-state clamp. RESULTS: Both groups had similar muscle and lean body mass indices and activity level. During insulin, glucose (8 mmol/L), and amino acid (AA; 2× fasting) infusions, serum insulin was lower in the elderly women and C-peptide increased less. Glucose uptake was stimulated, and production suppressed similarly. Suppression of whole-body protein breakdown was less in the elderly women, leading to lower AA infusion rates, oxidation, and net positive protein balance, but differences were not present when adjusted for serum insulin. Whole-body protein synthesis and muscle protein fractional synthesis rate increased similarly. Similar increases in phosphorylated Akt(Ser473), PRAS40(Thr246), FoxO3a(Thr32), and rpS6(Ser240/244) indicated no alterations in insulin/nutrient signaling with aging. CONCLUSIONS: Both whole-body and muscle fed-state protein anabolic responses were preserved, as was insulin sensitivity of glucose metabolism, in active, healthy elderly women. This is consistent with other factors such as sedentarity, low protein intake, and concurrent diseases, being responsible for the sarcopenia of aging.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".