Optimizing muscle mass and function in advanced lung cancer patients: randomized, double-blind, placebo-controlled trial protocol using High Eicosapentaenoic acid and PROtein (HEPRO) to modulate epigenetics, reduce toxicity and improve gut microbiota
Bibliographic record
Abstract
Background Lung cancer is strongly associated with malnutrition and detrimental changes in muscle mass (MM), which can lead to reduced quality of life and reduced tolerance to and efficacy of antineoplastic treatment. The loss of MM and myosteatosis (fat infiltration into muscle) have been linked to inflammation in cancer, and n-3 polyunsaturated fatty acids (PUFA) found in fish oil are known to modulate inflammatory response, lean mass, microbiota, and epigenetic mechanisms. Methods High Eicosapentaenoic acid and PROtein (HEPRO) is a randomized, double-blind, placebo-controlled clinical trial. A total of 50 patients over 20 years of age diagnosed with stage III or IV non-small cell lung cancer with an ECOG performance status of 0–2 who are eligible for systemic treatment will be included and randomized 1:1 into two treatment arms: four fish oil capsules containing 2,100 mg EPA and 924 mg DHA per day versus four placebo capsules containing 2,250 mg oleic acid per day for 4 months. All patients will be instructed to consume 1.5 g protein per kg body weight per day and receive protein supplementation if necessary. MM, myosteatosis, muscle function, handgrip strength, dietary assessment, toxicity, response to treatment, and survival will be assessed. Translational research includes membrane phospholipid composition, gut microbiota, inflammation, and miRNA. MicroRNA will be analyzed by quantitative real-time polymerase chain reaction, phospholipids, by gas chromatography, and microbiota, by 16S ribosomal RNA genetic sequencing. Statistical analysis will be conducted using IBM SPSS Statistics V.26 and a multiple regression model will be proposed. Associations with p < 0.05 will be considered significant. Conclusions The HEPRO study aims to evaluate a viable dietary intervention strategy to improve MM and function in patients with lung cancer.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".