Feasibility of two levels of protein intake in patients with colorectal cancer: findings from the Protein Recommendation to Increase Muscle (PRIMe) randomized controlled pilot trial
Bibliographic record
Abstract
BACKGROUND: Low muscle mass (MM) predicts unfavorable outcomes in cancer. Protein intake supports muscle health, but oncologic recommendations are not well characterized. The objectives of this study were to evaluate the feasibility of dietary change to attain 1.0 or 2.0 g/kg/day protein diets, and the preliminary potential to halt MM loss and functional decline in patients starting chemotherapy for stage II-IV colorectal cancer. PATIENTS AND METHODS: Patients were randomized to the diets and provided individualized counseling. Assessments at baseline, 6 weeks, and 12 weeks included weighed 3-day food records, appendicular lean soft tissue index (ALSTI) by dual-energy X-ray absorptiometry to estimate MM, and physical function by the Short Physical Performance Battery (SPPB) test. RESULTS: ; and protein intake, 1.1 ± 0.4 g/kg/day) were included at baseline. At week 12, protein intake reached 1.6 g/kg/day in the 2.0 g/kg/day group and 1.2 g/kg/day in the 1.0 g/kg/day group (P = 0.012), resulting in a group difference of 0.4 g/kg/day rather than 1.0 g/kg/day. Over one-half (59%) of patients in the 2.0 g/kg/day group maintained or gained MM compared with 44% of patients in the 1.0 g/kg/day group (P = 0.523). Percent change in ALSTI did not differ between groups [2.0 g/kg/day group (mean ± standard deviation): 0.5% ± 4.6%; 1.0 g/kg/day group: -0.4% ± 6.1%; P = 0.619]. No differences in physical function were observed between groups. However, actual protein intake and SPPB were positively associated (β = 0.37; 95% confidence interval 0.08-0.67; P = 0.014). CONCLUSION: Individualized nutrition counselling positively impacted protein intake. However, 2.0 g/kg/day was not attainable using our approach in this population, and group contamination occurred. Increased protein intake suggested positive effects on MM and physical function, highlighting the potential for nutrition to attenuate MM loss in patients with cancer. Nonetheless, muscle anabolism to any degree is clinically significant and beneficial to patients. Larger trials should explore the statistical significance and clinical relevance of protein interventions.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".