n‐3 fatty acids are reduced in advanced cancer patients with sarcopenia
Bibliographic record
Abstract
Little is known about the essential fatty acid status of cancer patients at diagnosis and throughout treatment. Sarcopenia (depletion of skeletal muscle) is associated with physical disability and mortality in aging and chronic diseases but the relationship between body composition and nutritional status in cancer is unclear. The objective of this study was to measure n‐3 and n‐6 fatty acids in plasma phospholipids of advanced non‐small cell lung cancer patients. Total skeletal muscle cross‐sectional area (n=22) was evaluated using lumbar CT images. Cut‐points [?: <55.4 cm 2 /m 2 ; ?: <38.9cm 2 /m 2 ; Mourtzakis et al Appl Physiol Nutr Metabol 2008] were used to classify subjects as sarcopenic (n=13) or non‐sarcopenic (n=9). Phospholipids were isolated from plasma lipids using thin layer chromatography. Amounts and types of fatty acids were determined using gas liquid chromatography. Total n‐3 fatty acids of sarcopenic subjects were lower than non‐sarcopenic subjects (16±11µg/mL versus 35±10 µg/mL, p =0.001), with 45% lower 20:5n‐3(p=0.005) and 54% lower 22:6n‐3 (p=0.0098). This resulted in a two‐fold higher n6:n3 ratio (p=0.05) in the sarcopenic versus non‐sarcopenic subjects. Cancer patients with sarcopenia have pronounced alterations in lipid metabolism, specifically n‐3 fatty acids, suggesting an important relationship between muscle atrophy and essential fatty acid metabolism. (Supported by CIHR). Grant Funding Source Canadian Institute for Health Research
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".