Defining anabolic resistance: implications for delivery of clinical care nutrition
Bibliographic record
Abstract
PURPOSE OF REVIEW: Skeletal muscle mass with aging, during critical care, and following critical care is a determinant of quality of life and survival. In this review, we discuss the mechanisms that underpin skeletal muscle atrophy and recommendations to offset skeletal muscle atrophy with aging and during, as well as following, critical care. RECENT FINDINGS: Anabolic resistance is responsible, in part, for skeletal muscle atrophy with aging, muscle disuse, and during disease states. Anabolic resistance describes the reduced stimulation of muscle protein synthesis to a given dose of protein/amino acids and contributes to declines in skeletal muscle mass. Physical inactivity induces: anabolic resistance (that is likely exacerbated with aging), insulin resistance, systemic inflammation, decreased satellite cell content, and decreased capillary density. Critical illness results in rapid skeletal muscle atrophy that is a result of both anabolic resistance and enhanced skeletal muscle breakdown. SUMMARY: Insofar as atrophic loss of skeletal muscle mass is concerned, anabolic resistance is a principal determinant of age-induced losses and appears to be a contributor to critical illness-induced skeletal muscle atrophy. Older individuals should perform exercise using both heavy and light loads three times per week, ingest at least 1.2 g of protein/kg/day, evenly distribute their meals into protein boluses of 0.40 g/kg, and consume protein within 2 h of retiring for sleep. During critical care, early, frequent, and multimodal physical therapies in combination with early, enteral, hypocaloric energy (∼10-15 kcal/kg/day), and high-protein (>1.2 g/kg/day) provision is recommended.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".