Systematic review and meta-analysis of the evidence for oral nutritional intervention on nutritional and clinical outcomes during chemo(radio)therapy: current evidence and guidance for design of future trials
Bibliographic record
Abstract
Background: Driven by reduced nutritional intakes and metabolic alterations, malnutrition in cancer patients adversely affects quality of life, treatment tolerance and survival. We examined evidence for oral nutritional interventions during chemo(radio)therapy. Design: We carried out a systematic review of randomized controlled trials (RCT) with either dietary counseling (DC), high-energy oral nutritional supplements (ONS) aiming at improving intakes or ONS enriched with protein and n-3 polyunsaturated fatty acids (PUFA) additionally aiming for modulation of cancer-related metabolic alterations. Meta-analyses were carried out on body weight (BW) response to nutritional interventions, with subgroup analyses for DC and/or high-energy ONS or high-protein n-3 PUFA-enriched ONS. Results: Eleven studies were identified. Meta-analysis showed overall benefit of interventions on BW during chemo(radio)therapy (+1.31 kg, 95% CI 0.24-2.38, P = 0.02, heterogeneity Q = 21.1, P = 0.007). Subgroup analysis showed no effect of DC and/or high-energy ONS (+0.80 kg, 95% CI -1.14 to 2.74, P = 0.32; Q = 10.5, P = 0.03), possibly due to limited compliance and intakes falling short of intake goals. A significant effect was observed for high-protein n-3 PUFA-enriched intervention compared with isocaloric controls (+1.89 kg, 95% CI 0.51-3.27, P = 0.02; Q = 3.1 P = 0.37). High-protein, n-3 PUFA-enriched ONS studies showed attenuation of lean body mass loss (N = 2 studies) and improvement of some quality of life domains (N = 3 studies). Overall, studies were limited in number, heterogeneous, and inadequately powered to show effects on treatment toxicity or survival. Conclusion: This systematic review suggests an overall positive effect of nutritional interventions during chemo(radio)therapy on BW. Subgroup analyses showed effects were driven by high-protein n-3 PUFA-enriched ONS, suggesting the benefit of targeting metabolic alterations. DC and/or high-energy ONS were less effective, likely due to cumulative caloric deficits despite interventions. We highlight the need and provide recommendations for well-designed RCT to determine the effect of nutritional interventions on clinical outcomes, with specific focus on reaching nutritional goals and providing the right nutrients, as part of an integral supportive care approach.
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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 | 0.017 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.024 | 0.033 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".